Overview

Dataset statistics

Number of variables53
Number of observations83861
Missing cells503168
Missing cells (%)11.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory33.9 MiB
Average record size in memory424.0 B

Variable types

Numeric22
Categorical25
Unsupported6

Warnings

customer_local_key has a high cardinality: 10580 distinct values High cardinality
branch_desc_cons has a high cardinality: 75 distinct values High cardinality
driver_name has a high cardinality: 27785 distinct values High cardinality
start_date_leasecontract has a high cardinality: 3782 distinct values High cardinality
end_date_leasecontract has a high cardinality: 3459 distinct values High cardinality
customer_driver_id has a high cardinality: 34378 distinct values High cardinality
vehicle_local_key has a high cardinality: 83861 distinct values High cardinality
registration_date has a high cardinality: 4215 distinct values High cardinality
model_cons has a high cardinality: 433 distinct values High cardinality
model_type_desc has a high cardinality: 8914 distinct values High cardinality
enriched_model has a high cardinality: 425 distinct values High cardinality
lease_contract_id is highly correlated with vehicle_idHigh correlation
vehicle_id is highly correlated with lease_contract_idHigh correlation
manufacturer_group is highly correlated with make_cons and 1 other fieldsHigh correlation
make_cons is highly correlated with manufacturer_group and 1 other fieldsHigh correlation
Customer_Segment is highly correlated with business_classification_descHigh correlation
segment_global is highly correlated with vehicle_type_cons and 1 other fieldsHigh correlation
enriched_make is highly correlated with manufacturer_group and 1 other fieldsHigh correlation
vehicle_segment_desc is highly correlated with vehicle_type_consHigh correlation
vehicle_type_cons is highly correlated with segment_global and 2 other fieldsHigh correlation
business_classification_desc is highly correlated with Customer_SegmentHigh correlation
segment_regional is highly correlated with segment_global and 1 other fieldsHigh correlation
Unnamed: 47 has 83861 (100.0%) missing values Missing
Unnamed: 48 has 83861 (100.0%) missing values Missing
Unnamed: 49 has 83861 (100.0%) missing values Missing
Unnamed: 50 has 83861 (100.0%) missing values Missing
Unnamed: 51 has 83861 (100.0%) missing values Missing
Unnamed: 52 has 83861 (100.0%) missing values Missing
vehicle_local_key is uniformly distributed Uniform
lease_contract_id has unique values Unique
leasecontract_number has unique values Unique
vehicle_id has unique values Unique
vehicle_local_key has unique values Unique
Unnamed: 47 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 48 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 49 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 50 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 51 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 52 is an unsupported type, check if it needs cleaning or further analysis Unsupported
fuel_consump_manufacturer has 49765 (59.3%) zeros Zeros
cat_price_options_euro has 37607 (44.8%) zeros Zeros
cat_price_accessories_euro has 64618 (77.1%) zeros Zeros
gross_weight has 1980 (2.4%) zeros Zeros
tare_weight has 5642 (6.7%) zeros Zeros
co2_level_combined has 3992 (4.8%) zeros Zeros
number_of_cylinders has 3673 (4.4%) zeros Zeros
number_of_doors has 20982 (25.0%) zeros Zeros
supplier_discount_perc has 44032 (52.5%) zeros Zeros
discount_to_customer has 17539 (20.9%) zeros Zeros

Reproduction

Analysis started2022-05-03 04:06:10.400870
Analysis finished2022-05-03 04:07:41.427456
Duration1 minute and 31.03 seconds
Software versionpandas-profiling v2.10.0
Download configurationconfig.yaml

Variables

srno
Real number (ℝ≥0)

Distinct6616
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1994.856429
Minimum0
Maximum6646
Zeros40
Zeros (%)< 0.1%
Memory size655.3 KiB
2022-05-03T12:07:41.538344image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile108
Q1593
median1575
Q33097
95-th percentile5306
Maximum6646
Range6646
Interquartile range (IQR)2504

Descriptive statistics

Standard deviation1646.718194
Coefficient of variation (CV)0.82548206
Kurtosis-0.3773184143
Mean1994.856429
Median Absolute Deviation (MAD)1129
Skewness0.7773796853
Sum167290655
Variance2711680.812
MonotocityNot monotonic
2022-05-03T12:07:41.647701image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16845
 
0.1%
17545
 
0.1%
16644
 
0.1%
17444
 
0.1%
16344
 
0.1%
12444
 
0.1%
9144
 
0.1%
16243
 
0.1%
9643
 
0.1%
9343
 
0.1%
Other values (6606)83422
99.5%
ValueCountFrequency (%)
040
< 0.1%
142
0.1%
241
< 0.1%
340
< 0.1%
437
< 0.1%
ValueCountFrequency (%)
66461
< 0.1%
66451
< 0.1%
66441
< 0.1%
66431
< 0.1%
66421
< 0.1%

customer_number
Real number (ℝ≥0)

Distinct10580
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean203629.6177
Minimum25
Maximum915070
Zeros0
Zeros (%)0.0%
Memory size655.3 KiB
2022-05-03T12:07:41.773708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile19943
Q133373
median36877
Q3340638
95-th percentile907054
Maximum915070
Range915045
Interquartile range (IQR)307265

Descriptive statistics

Standard deviation298788.1306
Coefficient of variation (CV)1.467311749
Kurtosis0.5970858869
Mean203629.6177
Median Absolute Deviation (MAD)11568
Skewness1.488576852
Sum1.707658337 × 1010
Variance8.9274347 × 1010
MonotocityNot monotonic
2022-05-03T12:07:41.883089image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
333732914
 
3.5%
336462589
 
3.1%
280672240
 
2.7%
208711897
 
2.3%
917701674
 
2.0%
334551640
 
2.0%
9070541335
 
1.6%
368311279
 
1.5%
43012933
 
1.1%
909427925
 
1.1%
Other values (10570)66435
79.2%
ValueCountFrequency (%)
252
 
< 0.1%
108025
< 0.1%
473345
0.1%
58025
 
< 0.1%
59615
 
< 0.1%
ValueCountFrequency (%)
9150708
 
< 0.1%
91503531
< 0.1%
9150326
 
< 0.1%
9150213
 
< 0.1%
9141008
 
< 0.1%

customer_id
Real number (ℝ≥0)

Distinct10580
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25924485.95
Minimum23485848
Maximum41108039
Zeros0
Zeros (%)0.0%
Memory size655.3 KiB
2022-05-03T12:07:41.992436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum23485848
5-th percentile23490209
Q123511969
median23598677
Q328387258
95-th percentile34975162
Maximum41108039
Range17622191
Interquartile range (IQR)4875289

Descriptive statistics

Standard deviation4104296.861
Coefficient of variation (CV)0.1583173865
Kurtosis1.744061272
Mean25924485.95
Median Absolute Deviation (MAD)97423
Skewness1.657312443
Sum2.174053316 × 1012
Variance1.684525273 × 1013
MonotocityNot monotonic
2022-05-03T12:07:42.101786image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
235121002914
 
3.5%
235119692589
 
3.1%
234868022240
 
2.7%
234920641897
 
2.3%
236073241674
 
2.0%
235123701640
 
2.0%
236182921335
 
1.6%
294586321279
 
1.5%
23541849933
 
1.1%
23605624925
 
1.1%
Other values (10570)66435
79.2%
ValueCountFrequency (%)
234858482
< 0.1%
234858632
< 0.1%
234858652
< 0.1%
234858933
< 0.1%
234859141
 
< 0.1%
ValueCountFrequency (%)
411080395
 
< 0.1%
410843555
 
< 0.1%
410375615
 
< 0.1%
409661992
 
< 0.1%
4091366720
< 0.1%

customer_local_key
Categorical

HIGH CARDINALITY

Distinct10580
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Memory size655.3 KiB
UK-C-33373
 
2914
UK-C-33646
 
2589
UK-C-28067
 
2240
UK-C-20871
 
1897
UK-C-91770
 
1674
Other values (10575)
72547 

Length

Max length11
Median length10
Mean length10.25371746
Min length7

Characters and Unicode

Total characters859887
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1506 ?
Unique (%)1.8%

Sample

1st rowUK-C-506550
2nd rowUK-C-506550
3rd rowUK-C-841984
4th rowUK-C-841984
5th rowUK-C-850742
ValueCountFrequency (%)
UK-C-333732914
 
3.5%
UK-C-336462589
 
3.1%
UK-C-280672240
 
2.7%
UK-C-208711897
 
2.3%
UK-C-917701674
 
2.0%
UK-C-334551640
 
2.0%
UK-C-9070541335
 
1.6%
UK-C-368311279
 
1.5%
UK-C-43012933
 
1.1%
UK-C-909427925
 
1.1%
Other values (10570)66435
79.2%
2022-05-03T12:07:42.351726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
uk-c-333732914
 
3.5%
uk-c-336462589
 
3.1%
uk-c-280672240
 
2.7%
uk-c-208711897
 
2.3%
uk-c-917701674
 
2.0%
uk-c-334551640
 
2.0%
uk-c-9070541335
 
1.6%
uk-c-368311279
 
1.5%
uk-c-43012933
 
1.1%
uk-c-909427925
 
1.1%
Other values (10570)66435
79.2%

Most occurring characters

ValueCountFrequency (%)
-167722
19.5%
U83861
9.8%
K83861
9.8%
C83861
9.8%
379403
9.2%
449503
 
5.8%
743518
 
5.1%
042574
 
5.0%
239537
 
4.6%
938288
 
4.5%
Other values (4)147759
17.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number440582
51.2%
Uppercase Letter251583
29.3%
Dash Punctuation167722
 
19.5%

Most frequent character per category

ValueCountFrequency (%)
379403
18.0%
449503
11.2%
743518
9.9%
042574
9.7%
239537
9.0%
938288
8.7%
138174
8.7%
637823
8.6%
836680
8.3%
535082
8.0%
ValueCountFrequency (%)
U83861
33.3%
K83861
33.3%
C83861
33.3%
ValueCountFrequency (%)
-167722
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common608304
70.7%
Latin251583
29.3%

Most frequent character per script

ValueCountFrequency (%)
-167722
27.6%
379403
13.1%
449503
 
8.1%
743518
 
7.2%
042574
 
7.0%
239537
 
6.5%
938288
 
6.3%
138174
 
6.3%
637823
 
6.2%
836680
 
6.0%
ValueCountFrequency (%)
U83861
33.3%
K83861
33.3%
C83861
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII859887
100.0%

Most frequent character per block

ValueCountFrequency (%)
-167722
19.5%
U83861
9.8%
K83861
9.8%
C83861
9.8%
379403
9.2%
449503
 
5.8%
743518
 
5.1%
042574
 
5.0%
239537
 
4.6%
938288
 
4.5%
Other values (4)147759
17.2%

branch_desc_cons
Categorical

HIGH CARDINALITY

Distinct75
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size655.3 KiB
Business Services
14307 
Not consolidated
7222 
Private Households
6729 
Wholesale Trade--Durable Goods
4923 
Electric, Gas, And Sanitary Services (Private Companies)
 
4004
Other values (70)
46676 

Length

Max length59
Median length18
Mean length24.65413005
Min length7

Characters and Unicode

Total characters2067520
Distinct characters51
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFurniture And Fixtures
2nd rowFurniture And Fixtures
3rd rowPrivate Households
4th rowPrivate Households
5th rowSpecial Trade Contractors
ValueCountFrequency (%)
Business Services14307
17.1%
Not consolidated7222
 
8.6%
Private Households6729
 
8.0%
Wholesale Trade--Durable Goods4923
 
5.9%
Electric, Gas, And Sanitary Services (Private Companies)4004
 
4.8%
Insurance Carriers3627
 
4.3%
Real Estate3257
 
3.9%
Special Trade Contractors2936
 
3.5%
Health Services2869
 
3.4%
Wholesale Trade--Nondurable Goods2779
 
3.3%
Other values (65)31208
37.2%
2022-05-03T12:07:42.601668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
services26139
 
10.6%
and14997
 
6.1%
business14307
 
5.8%
private10733
 
4.3%
goods8994
 
3.6%
7754
 
3.1%
wholesale7702
 
3.1%
not7222
 
2.9%
consolidated7222
 
2.9%
households6729
 
2.7%
Other values (143)135462
54.8%

Most occurring characters

ValueCountFrequency (%)
e211999
 
10.3%
s167652
 
8.1%
163400
 
7.9%
r139345
 
6.7%
i134949
 
6.5%
a132382
 
6.4%
o129958
 
6.3%
t107933
 
5.2%
n106715
 
5.2%
c97675
 
4.7%
Other values (41)675512
32.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1617753
78.2%
Uppercase Letter239955
 
11.6%
Space Separator163400
 
7.9%
Other Punctuation23000
 
1.1%
Dash Punctuation15404
 
0.7%
Open Punctuation4004
 
0.2%
Close Punctuation4004
 
0.2%

Most frequent character per category

ValueCountFrequency (%)
e211999
13.1%
s167652
10.4%
r139345
8.6%
i134949
8.3%
a132382
8.2%
o129958
8.0%
t107933
 
6.7%
n106715
 
6.6%
c97675
 
6.0%
l89958
 
5.6%
Other values (14)299187
18.5%
ValueCountFrequency (%)
S38116
15.9%
C29382
12.2%
P23645
9.9%
A20044
8.4%
E17006
 
7.1%
B15727
 
6.6%
G14819
 
6.2%
T13474
 
5.6%
H12613
 
5.3%
N10088
 
4.2%
Other values (10)45041
18.8%
ValueCountFrequency (%)
,14837
64.5%
&7754
33.7%
.409
 
1.8%
ValueCountFrequency (%)
163400
100.0%
ValueCountFrequency (%)
-15404
100.0%
ValueCountFrequency (%)
(4004
100.0%
ValueCountFrequency (%)
)4004
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1857708
89.9%
Common209812
 
10.1%

Most frequent character per script

ValueCountFrequency (%)
e211999
 
11.4%
s167652
 
9.0%
r139345
 
7.5%
i134949
 
7.3%
a132382
 
7.1%
o129958
 
7.0%
t107933
 
5.8%
n106715
 
5.7%
c97675
 
5.3%
l89958
 
4.8%
Other values (34)539142
29.0%
ValueCountFrequency (%)
163400
77.9%
-15404
 
7.3%
,14837
 
7.1%
&7754
 
3.7%
(4004
 
1.9%
)4004
 
1.9%
.409
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII2067520
100.0%

Most frequent character per block

ValueCountFrequency (%)
e211999
 
10.3%
s167652
 
8.1%
163400
 
7.9%
r139345
 
6.7%
i134949
 
6.5%
a132382
 
6.4%
o129958
 
6.3%
t107933
 
5.2%
n106715
 
5.2%
c97675
 
4.7%
Other values (41)675512
32.7%

business_classification_desc
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size655.3 KiB
Corporate
45877 
Small fleet
20402 
Governments
7155 
Private Households
6729 
Insurance companies
 
3608
Other values (2)
 
90

Length

Max length19
Median length9
Mean length10.80540418
Min length5

Characters and Unicode

Total characters906152
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSmall fleet
2nd rowSmall fleet
3rd rowPrivate Households
4th rowPrivate Households
5th rowSmall fleet
ValueCountFrequency (%)
Corporate45877
54.7%
Small fleet20402
24.3%
Governments7155
 
8.5%
Private Households6729
 
8.0%
Insurance companies3608
 
4.3%
Banks88
 
0.1%
Own fleet2
 
< 0.1%
2022-05-03T12:07:42.774793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2022-05-03T12:07:42.837279image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
corporate45877
40.0%
fleet20404
17.8%
small20402
17.8%
governments7155
 
6.2%
private6729
 
5.9%
households6729
 
5.9%
insurance3608
 
3.1%
companies3608
 
3.1%
banks88
 
0.1%
own2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e121669
13.4%
o115975
12.8%
r109246
12.1%
a80312
8.9%
t80165
8.8%
l67937
 
7.5%
p49485
 
5.5%
C45877
 
5.1%
m31165
 
3.4%
30741
 
3.4%
Other values (18)173580
19.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter784821
86.6%
Uppercase Letter90590
 
10.0%
Space Separator30741
 
3.4%

Most frequent character per category

ValueCountFrequency (%)
e121669
15.5%
o115975
14.8%
r109246
13.9%
a80312
10.2%
t80165
10.2%
l67937
8.7%
p49485
6.3%
m31165
 
4.0%
s27917
 
3.6%
n25224
 
3.2%
Other values (9)75726
9.6%
ValueCountFrequency (%)
C45877
50.6%
S20402
22.5%
G7155
 
7.9%
P6729
 
7.4%
H6729
 
7.4%
I3608
 
4.0%
B88
 
0.1%
O2
 
< 0.1%
ValueCountFrequency (%)
30741
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin875411
96.6%
Common30741
 
3.4%

Most frequent character per script

ValueCountFrequency (%)
e121669
13.9%
o115975
13.2%
r109246
12.5%
a80312
9.2%
t80165
9.2%
l67937
7.8%
p49485
 
5.7%
C45877
 
5.2%
m31165
 
3.6%
s27917
 
3.2%
Other values (17)145663
16.6%
ValueCountFrequency (%)
30741
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII906152
100.0%

Most frequent character per block

ValueCountFrequency (%)
e121669
13.4%
o115975
12.8%
r109246
12.1%
a80312
8.9%
t80165
8.8%
l67937
 
7.5%
p49485
 
5.5%
C45877
 
5.1%
m31165
 
3.4%
30741
 
3.4%
Other values (18)173580
19.2%

driver_name
Categorical

HIGH CARDINALITY

Distinct27785
Distinct (%)33.1%
Missing0
Missing (%)0.0%
Memory size655.3 KiB
Network Driver
8385 
POOL
 
711
CARPOOL
 
705
TBC TBC
 
616
UNSPECIFIED
 
549
Other values (27780)
72895 

Length

Max length36
Median length13
Mean length13.52125541
Min length1

Characters and Unicode

Total characters1133906
Distinct characters76
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4443 ?
Unique (%)5.3%

Sample

1st rowtoby downes
2nd rowtoby downes
3rd rowthomas fosmes
4th rowthomas fosmes
5th rowterry cottam
ValueCountFrequency (%)
Network Driver8385
 
10.0%
POOL711
 
0.8%
CARPOOL705
 
0.8%
TBC TBC616
 
0.7%
UNSPECIFIED549
 
0.7%
TBA TBA428
 
0.5%
POOL CAR401
 
0.5%
POOL POOL368
 
0.4%
POOL VEHICLE340
 
0.4%
POOL VAN326
 
0.4%
Other values (27775)71032
84.7%
2022-05-03T12:07:43.118460image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pool10481
 
5.8%
driver8688
 
4.8%
network8446
 
4.7%
auction3101
 
1.7%
return2316
 
1.3%
paul1815
 
1.0%
david1791
 
1.0%
c/o1724
 
1.0%
mark1492
 
0.8%
andrew1472
 
0.8%
Other values (15826)137954
76.9%

Most occurring characters

ValueCountFrequency (%)
95458
 
8.4%
A74578
 
6.6%
E72897
 
6.4%
O71914
 
6.3%
N68249
 
6.0%
R63381
 
5.6%
L56353
 
5.0%
I48106
 
4.2%
T45222
 
4.0%
S38489
 
3.4%
Other values (66)499259
44.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter812775
71.7%
Lowercase Letter164942
 
14.5%
Space Separator95458
 
8.4%
Decimal Number55642
 
4.9%
Other Punctuation3779
 
0.3%
Dash Punctuation1066
 
0.1%
Connector Punctuation88
 
< 0.1%
Open Punctuation77
 
< 0.1%
Close Punctuation76
 
< 0.1%
Math Symbol3
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
r31614
19.2%
e25227
15.3%
i13979
8.5%
o13428
8.1%
t12404
 
7.5%
k9461
 
5.7%
w9399
 
5.7%
v9163
 
5.6%
a8842
 
5.4%
n6533
 
4.0%
Other values (16)24892
15.1%
ValueCountFrequency (%)
A74578
 
9.2%
E72897
 
9.0%
O71914
 
8.8%
N68249
 
8.4%
R63381
 
7.8%
L56353
 
6.9%
I48106
 
5.9%
T45222
 
5.6%
S38489
 
4.7%
C38235
 
4.7%
Other values (16)235351
29.0%
ValueCountFrequency (%)
112460
22.4%
011346
20.4%
35588
10.0%
25126
9.2%
44733
 
8.5%
53829
 
6.9%
73602
 
6.5%
83366
 
6.0%
63331
 
6.0%
92261
 
4.1%
ValueCountFrequency (%)
/2359
62.4%
.927
 
24.5%
'306
 
8.1%
&83
 
2.2%
:54
 
1.4%
*31
 
0.8%
#10
 
0.3%
,9
 
0.2%
ValueCountFrequency (%)
95458
100.0%
ValueCountFrequency (%)
-1066
100.0%
ValueCountFrequency (%)
_88
100.0%
ValueCountFrequency (%)
(77
100.0%
ValueCountFrequency (%)
)76
100.0%
ValueCountFrequency (%)
+3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin977717
86.2%
Common156189
 
13.8%

Most frequent character per script

ValueCountFrequency (%)
A74578
 
7.6%
E72897
 
7.5%
O71914
 
7.4%
N68249
 
7.0%
R63381
 
6.5%
L56353
 
5.8%
I48106
 
4.9%
T45222
 
4.6%
S38489
 
3.9%
C38235
 
3.9%
Other values (42)400293
40.9%
ValueCountFrequency (%)
95458
61.1%
112460
 
8.0%
011346
 
7.3%
35588
 
3.6%
25126
 
3.3%
44733
 
3.0%
53829
 
2.5%
73602
 
2.3%
83366
 
2.2%
63331
 
2.1%
Other values (14)7350
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1133906
100.0%

Most frequent character per block

ValueCountFrequency (%)
95458
 
8.4%
A74578
 
6.6%
E72897
 
6.4%
O71914
 
6.3%
N68249
 
6.0%
R63381
 
5.6%
L56353
 
5.0%
I48106
 
4.2%
T45222
 
4.0%
S38489
 
3.4%
Other values (66)499259
44.0%

start_date_leasecontract
Categorical

HIGH CARDINALITY

Distinct3782
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size655.3 KiB
2012-12-01 00:00:00
 
272
2014-06-01 00:00:00
 
239
2013-07-01 00:00:00
 
215
2015-06-01 00:00:00
 
188
2020-04-01 00:00:00
 
176
Other values (3777)
82771 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters1593359
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique169 ?
Unique (%)0.2%

Sample

1st row2014-03-10 00:00:00
2nd row2018-04-10 00:00:00
3rd row2016-04-25 00:00:00
4th row2018-05-25 00:00:00
5th row2016-09-26 00:00:00
ValueCountFrequency (%)
2012-12-01 00:00:00272
 
0.3%
2014-06-01 00:00:00239
 
0.3%
2013-07-01 00:00:00215
 
0.3%
2015-06-01 00:00:00188
 
0.2%
2020-04-01 00:00:00176
 
0.2%
2012-11-01 00:00:00165
 
0.2%
2012-06-01 00:00:00156
 
0.2%
2013-03-01 00:00:00154
 
0.2%
2013-06-01 00:00:00140
 
0.2%
2017-03-01 00:00:00127
 
0.2%
Other values (3772)82029
97.8%
2022-05-03T12:07:43.352784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:0083861
50.0%
2012-12-01272
 
0.2%
2014-06-01239
 
0.1%
2013-07-01215
 
0.1%
2015-06-01188
 
0.1%
2020-04-01176
 
0.1%
2012-11-01165
 
0.1%
2012-06-01156
 
0.1%
2013-03-01154
 
0.1%
2013-06-01140
 
0.1%
Other values (3773)82156
49.0%

Most occurring characters

ValueCountFrequency (%)
0703459
44.1%
-167722
 
10.5%
:167722
 
10.5%
1160500
 
10.1%
2141907
 
8.9%
83861
 
5.3%
329649
 
1.9%
625871
 
1.6%
525282
 
1.6%
422795
 
1.4%
Other values (3)64591
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1174054
73.7%
Dash Punctuation167722
 
10.5%
Other Punctuation167722
 
10.5%
Space Separator83861
 
5.3%

Most frequent character per category

ValueCountFrequency (%)
0703459
59.9%
1160500
 
13.7%
2141907
 
12.1%
329649
 
2.5%
625871
 
2.2%
525282
 
2.2%
422795
 
1.9%
722733
 
1.9%
921617
 
1.8%
820241
 
1.7%
ValueCountFrequency (%)
-167722
100.0%
ValueCountFrequency (%)
83861
100.0%
ValueCountFrequency (%)
:167722
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1593359
100.0%

Most frequent character per script

ValueCountFrequency (%)
0703459
44.1%
-167722
 
10.5%
:167722
 
10.5%
1160500
 
10.1%
2141907
 
8.9%
83861
 
5.3%
329649
 
1.9%
625871
 
1.6%
525282
 
1.6%
422795
 
1.4%
Other values (3)64591
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1593359
100.0%

Most frequent character per block

ValueCountFrequency (%)
0703459
44.1%
-167722
 
10.5%
:167722
 
10.5%
1160500
 
10.1%
2141907
 
8.9%
83861
 
5.3%
329649
 
1.9%
625871
 
1.6%
525282
 
1.6%
422795
 
1.4%
Other values (3)64591
 
4.1%

end_date_leasecontract
Categorical

HIGH CARDINALITY

Distinct3459
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size655.3 KiB
###############################################################################################################################################################################################################################################################
22060 
2020-04-30 00:00:00
 
398
2020-03-31 00:00:00
 
344
2013-06-30 00:00:00
 
191
2015-05-31 00:00:00
 
171
Other values (3454)
60697 

Length

Max length255
Median length19
Mean length81.08082422
Min length19

Characters and Unicode

Total characters6799519
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique212 ?
Unique (%)0.3%

Sample

1st row2018-04-09 00:00:00
2nd row2019-04-08 00:00:00
3rd row2018-05-24 00:00:00
4th row2018-10-08 00:00:00
5th row2019-09-25 00:00:00
ValueCountFrequency (%)
###############################################################################################################################################################################################################################################################22060
 
26.3%
2020-04-30 00:00:00398
 
0.5%
2020-03-31 00:00:00344
 
0.4%
2013-06-30 00:00:00191
 
0.2%
2015-05-31 00:00:00171
 
0.2%
2013-05-31 00:00:00108
 
0.1%
2014-05-31 00:00:00102
 
0.1%
2017-02-28 00:00:00101
 
0.1%
2013-11-14 00:00:0089
 
0.1%
2016-09-30 00:00:0086
 
0.1%
Other values (3449)60211
71.8%
2022-05-03T12:07:43.555859image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:0061801
42.4%
22060
 
15.1%
2020-04-30398
 
0.3%
2020-03-31344
 
0.2%
2013-06-30191
 
0.1%
2015-05-31171
 
0.1%
2013-05-31108
 
0.1%
2014-05-31102
 
0.1%
2017-02-28101
 
0.1%
2013-11-1489
 
0.1%
Other values (3450)60297
41.4%

Most occurring characters

ValueCountFrequency (%)
#5625300
82.7%
0516118
 
7.6%
-123602
 
1.8%
:123602
 
1.8%
1109701
 
1.6%
2103127
 
1.5%
61801
 
0.9%
321343
 
0.3%
920321
 
0.3%
619778
 
0.3%
Other values (4)74826
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation5748902
84.5%
Decimal Number865214
 
12.7%
Dash Punctuation123602
 
1.8%
Space Separator61801
 
0.9%

Most frequent character per category

ValueCountFrequency (%)
0516118
59.7%
1109701
 
12.7%
2103127
 
11.9%
321343
 
2.5%
920321
 
2.3%
619778
 
2.3%
718996
 
2.2%
518943
 
2.2%
418446
 
2.1%
818441
 
2.1%
ValueCountFrequency (%)
#5625300
97.8%
:123602
 
2.2%
ValueCountFrequency (%)
-123602
100.0%
ValueCountFrequency (%)
61801
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common6799519
100.0%

Most frequent character per script

ValueCountFrequency (%)
#5625300
82.7%
0516118
 
7.6%
-123602
 
1.8%
:123602
 
1.8%
1109701
 
1.6%
2103127
 
1.5%
61801
 
0.9%
321343
 
0.3%
920321
 
0.3%
619778
 
0.3%
Other values (4)74826
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII6799519
100.0%

Most frequent character per block

ValueCountFrequency (%)
#5625300
82.7%
0516118
 
7.6%
-123602
 
1.8%
:123602
 
1.8%
1109701
 
1.6%
2103127
 
1.5%
61801
 
0.9%
321343
 
0.3%
920321
 
0.3%
619778
 
0.3%
Other values (4)74826
 
1.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size655.3 KiB
Ended
61801 
Operational
22057 
On order
 
3

Length

Max length11
Median length5
Mean length6.578218719
Min length5

Characters and Unicode

Total characters551656
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEnded
2nd rowEnded
3rd rowEnded
4th rowEnded
5th rowEnded
ValueCountFrequency (%)
Ended61801
73.7%
Operational22057
 
26.3%
On order3
 
< 0.1%
2022-05-03T12:07:43.728295image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2022-05-03T12:07:43.790783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
ended61801
73.7%
operational22057
 
26.3%
on3
 
< 0.1%
order3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
d123605
22.4%
n83861
15.2%
e83861
15.2%
E61801
11.2%
a44114
 
8.0%
r22063
 
4.0%
O22060
 
4.0%
o22060
 
4.0%
p22057
 
4.0%
t22057
 
4.0%
Other values (3)44117
 
8.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter467792
84.8%
Uppercase Letter83861
 
15.2%
Space Separator3
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
d123605
26.4%
n83861
17.9%
e83861
17.9%
a44114
 
9.4%
r22063
 
4.7%
o22060
 
4.7%
p22057
 
4.7%
t22057
 
4.7%
i22057
 
4.7%
l22057
 
4.7%
ValueCountFrequency (%)
E61801
73.7%
O22060
 
26.3%
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin551653
> 99.9%
Common3
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
d123605
22.4%
n83861
15.2%
e83861
15.2%
E61801
11.2%
a44114
 
8.0%
r22063
 
4.0%
O22060
 
4.0%
o22060
 
4.0%
p22057
 
4.0%
t22057
 
4.0%
Other values (2)44114
 
8.0%
ValueCountFrequency (%)
3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII551656
100.0%

Most frequent character per block

ValueCountFrequency (%)
d123605
22.4%
n83861
15.2%
e83861
15.2%
E61801
11.2%
a44114
 
8.0%
r22063
 
4.0%
O22060
 
4.0%
o22060
 
4.0%
p22057
 
4.0%
t22057
 
4.0%
Other values (3)44117
 
8.0%

customer_driver_id
Categorical

HIGH CARDINALITY

Distinct34378
Distinct (%)41.0%
Missing2
Missing (%)< 0.1%
Memory size655.3 KiB
UNSPEC
 
637
Pool
 
543
POOL
 
504
CARPOOL
 
405
POOL1
 
391
Other values (34373)
81379 

Length

Max length20
Median length8
Mean length10.31539847
Min length1

Characters and Unicode

Total characters865039
Distinct characters80
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6242 ?
Unique (%)7.4%

Sample

1st row00000000000004999185
2nd row00000000000004999185
3rd row00000000000005174754
4th row00000000000005174754
5th row00000000000005210435
ValueCountFrequency (%)
UNSPEC637
 
0.8%
Pool543
 
0.6%
POOL504
 
0.6%
CARPOOL405
 
0.5%
POOL1391
 
0.5%
POOLVAN235
 
0.3%
AUCTIONRETURN190
 
0.2%
pool189
 
0.2%
1188
 
0.2%
AUCTION RETURN155
 
0.2%
Other values (34368)80422
95.9%
2022-05-03T12:07:44.043602image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pool3120
 
3.3%
unspec781
 
0.8%
return690
 
0.7%
auction644
 
0.7%
carpool595
 
0.6%
pool1472
 
0.5%
smh452
 
0.5%
1427
 
0.5%
poolvan298
 
0.3%
car243
 
0.3%
Other values (33932)85527
91.7%

Most occurring characters

ValueCountFrequency (%)
0276015
31.9%
145988
 
5.3%
536255
 
4.2%
433138
 
3.8%
232135
 
3.7%
730404
 
3.5%
329758
 
3.4%
627424
 
3.2%
826917
 
3.1%
926815
 
3.1%
Other values (70)300190
34.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number564849
65.3%
Uppercase Letter235438
27.2%
Lowercase Letter53030
 
6.1%
Space Separator9395
 
1.1%
Other Punctuation1717
 
0.2%
Dash Punctuation591
 
0.1%
Close Punctuation8
 
< 0.1%
Math Symbol5
 
< 0.1%
Open Punctuation4
 
< 0.1%
Currency Symbol2
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
O24646
 
10.5%
A17846
 
7.6%
R16858
 
7.2%
E16364
 
7.0%
L16358
 
6.9%
C16153
 
6.9%
N15827
 
6.7%
T13912
 
5.9%
P13879
 
5.9%
S11355
 
4.8%
Other values (17)72240
30.7%
ValueCountFrequency (%)
o8132
15.3%
e5503
10.4%
l5290
10.0%
a4439
 
8.4%
r4273
 
8.1%
n3828
 
7.2%
i3028
 
5.7%
t2771
 
5.2%
s2717
 
5.1%
c1724
 
3.3%
Other values (16)11325
21.4%
ValueCountFrequency (%)
0276015
48.9%
145988
 
8.1%
536255
 
6.4%
433138
 
5.9%
232135
 
5.7%
730404
 
5.4%
329758
 
5.3%
627424
 
4.9%
826917
 
4.8%
926815
 
4.7%
ValueCountFrequency (%)
/1061
61.8%
.341
 
19.9%
*116
 
6.8%
!69
 
4.0%
,38
 
2.2%
'38
 
2.2%
&25
 
1.5%
#19
 
1.1%
;8
 
0.5%
?2
 
0.1%
ValueCountFrequency (%)
)4
50.0%
]4
50.0%
ValueCountFrequency (%)
9395
100.0%
ValueCountFrequency (%)
-591
100.0%
ValueCountFrequency (%)
+5
100.0%
ValueCountFrequency (%)
(4
100.0%
ValueCountFrequency (%)
£2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common576571
66.7%
Latin288468
33.3%

Most frequent character per script

ValueCountFrequency (%)
O24646
 
8.5%
A17846
 
6.2%
R16858
 
5.8%
E16364
 
5.7%
L16358
 
5.7%
C16153
 
5.6%
N15827
 
5.5%
T13912
 
4.8%
P13879
 
4.8%
S11355
 
3.9%
Other values (43)125270
43.4%
ValueCountFrequency (%)
0276015
47.9%
145988
 
8.0%
536255
 
6.3%
433138
 
5.7%
232135
 
5.6%
730404
 
5.3%
329758
 
5.2%
627424
 
4.8%
826917
 
4.7%
926815
 
4.7%
Other values (17)11722
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII865035
> 99.9%
None4
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
0276015
31.9%
145988
 
5.3%
536255
 
4.2%
433138
 
3.8%
232135
 
3.7%
730404
 
3.5%
329758
 
3.4%
627424
 
3.2%
826917
 
3.1%
926815
 
3.1%
Other values (68)300186
34.7%
ValueCountFrequency (%)
Â2
50.0%
£2
50.0%

lease_contract_id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct83861
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean226362286.2
Minimum133487525
Maximum393841442
Zeros0
Zeros (%)0.0%
Memory size655.3 KiB
2022-05-03T12:07:44.187272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum133487525
5-th percentile133559809
Q1145944277
median180316621
Q3305582607
95-th percentile371542115
Maximum393841442
Range260353917
Interquartile range (IQR)159638330

Descriptive statistics

Standard deviation86153066.95
Coefficient of variation (CV)0.3805981483
Kurtosis-1.302507862
Mean226362286.2
Median Absolute Deviation (MAD)46769751
Skewness0.4306453486
Sum1.898296768 × 1013
Variance7.422350944 × 1015
MonotocityNot monotonic
2022-05-03T12:07:44.327900image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3477360631
 
< 0.1%
2423199731
 
< 0.1%
1591711491
 
< 0.1%
1337212091
 
< 0.1%
1473357601
 
< 0.1%
1337022251
 
< 0.1%
3629144201
 
< 0.1%
1335752511
 
< 0.1%
1336960841
 
< 0.1%
1591220051
 
< 0.1%
Other values (83851)83851
> 99.9%
ValueCountFrequency (%)
1334875251
< 0.1%
1335051371
< 0.1%
1335216191
< 0.1%
1335221351
< 0.1%
1335223111
< 0.1%
ValueCountFrequency (%)
3938414421
< 0.1%
3938414281
< 0.1%
3938414201
< 0.1%
3938414141
< 0.1%
3938413901
< 0.1%

leasecontract_number
Real number (ℝ≥0)

UNIQUE

Distinct83861
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6021723.414
Minimum1000011
Maximum9999489
Zeros0
Zeros (%)0.0%
Memory size655.3 KiB
2022-05-03T12:07:44.490174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1000011
5-th percentile1036110
Q11181196
median8868962
Q39825950
95-th percentile9960990
Maximum9999489
Range8999478
Interquartile range (IQR)8644754

Descriptive statistics

Standard deviation4159106.568
Coefficient of variation (CV)0.6906837598
Kurtosis-1.882188583
Mean6021723.414
Median Absolute Deviation (MAD)1075132
Skewness-0.2997551434
Sum5.049877473 × 1011
Variance1.729816744 × 1013
MonotocityNot monotonic
2022-05-03T12:07:44.599522image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86727591
 
< 0.1%
13302211
 
< 0.1%
17761181
 
< 0.1%
11391931
 
< 0.1%
97886421
 
< 0.1%
98821751
 
< 0.1%
98775501
 
< 0.1%
98657321
 
< 0.1%
98636851
 
< 0.1%
10855611
 
< 0.1%
Other values (83851)83851
> 99.9%
ValueCountFrequency (%)
10000111
< 0.1%
10000351
< 0.1%
10000361
< 0.1%
10000421
< 0.1%
10000451
< 0.1%
ValueCountFrequency (%)
99994891
< 0.1%
99994751
< 0.1%
99994541
< 0.1%
99994421
< 0.1%
99994301
< 0.1%

vehicle_id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct83861
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean370778655
Minimum244461685
Maximum503292427
Zeros0
Zeros (%)0.0%
Memory size655.3 KiB
2022-05-03T12:07:44.725745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum244461685
5-th percentile244724734
Q1322464917
median380105016
Q3440276238
95-th percentile488921700
Maximum503292427
Range258830742
Interquartile range (IQR)117811321

Descriptive statistics

Standard deviation82703025.93
Coefficient of variation (CV)0.2230522842
Kurtosis-1.130434276
Mean370778655
Median Absolute Deviation (MAD)58394464
Skewness-0.2702663046
Sum3.109386879 × 1013
Variance6.839790498 × 1015
MonotocityNot monotonic
2022-05-03T12:07:44.850716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4470886391
 
< 0.1%
3617163501
 
< 0.1%
3391629011
 
< 0.1%
3250345931
 
< 0.1%
3214895081
 
< 0.1%
3263854561
 
< 0.1%
4166518781
 
< 0.1%
3665803271
 
< 0.1%
2447058961
 
< 0.1%
2447890141
 
< 0.1%
Other values (83851)83851
> 99.9%
ValueCountFrequency (%)
2444616851
< 0.1%
2444617081
< 0.1%
2444617101
< 0.1%
2444617121
< 0.1%
2444617211
< 0.1%
ValueCountFrequency (%)
5032924271
< 0.1%
5032924061
< 0.1%
5032923641
< 0.1%
5032923561
< 0.1%
5032923351
< 0.1%

vehicle_local_key
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct83861
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size655.3 KiB
UK-9208474
 
1
UK-1120645
 
1
UK-9760950
 
1
UK-1283661
 
1
UK-9876472
 
1
Other values (83856)
83856 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters838610
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83861 ?
Unique (%)100.0%

Sample

1st rowUK-9877167
2nd rowUK-1177401
3rd rowUK-1051210
4th rowUK-1183042
5th rowUK-1083812
ValueCountFrequency (%)
UK-92084741
 
< 0.1%
UK-11206451
 
< 0.1%
UK-97609501
 
< 0.1%
UK-12836611
 
< 0.1%
UK-98764721
 
< 0.1%
UK-98882971
 
< 0.1%
UK-87753911
 
< 0.1%
UK-86140041
 
< 0.1%
UK-12881861
 
< 0.1%
UK-85885791
 
< 0.1%
Other values (83851)83851
> 99.9%
2022-05-03T12:07:45.210006image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
uk-98166551
 
< 0.1%
uk-11964741
 
< 0.1%
uk-98854381
 
< 0.1%
uk-96592491
 
< 0.1%
uk-98560951
 
< 0.1%
uk-12852731
 
< 0.1%
uk-93762841
 
< 0.1%
uk-98558011
 
< 0.1%
uk-99074911
 
< 0.1%
uk-85879151
 
< 0.1%
Other values (83851)83851
> 99.9%

Most occurring characters

ValueCountFrequency (%)
995328
11.4%
189503
10.7%
U83861
10.0%
K83861
10.0%
-83861
10.0%
865271
7.8%
055684
6.6%
251289
 
6.1%
348646
 
5.8%
747562
 
5.7%
Other values (3)133744
15.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number587027
70.0%
Uppercase Letter167722
 
20.0%
Dash Punctuation83861
 
10.0%

Most frequent character per category

ValueCountFrequency (%)
995328
16.2%
189503
15.2%
865271
11.1%
055684
9.5%
251289
8.7%
348646
8.3%
747562
8.1%
445353
7.7%
544535
7.6%
643856
7.5%
ValueCountFrequency (%)
U83861
50.0%
K83861
50.0%
ValueCountFrequency (%)
-83861
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common670888
80.0%
Latin167722
 
20.0%

Most frequent character per script

ValueCountFrequency (%)
995328
14.2%
189503
13.3%
-83861
12.5%
865271
9.7%
055684
8.3%
251289
7.6%
348646
7.3%
747562
7.1%
445353
6.8%
544535
6.6%
ValueCountFrequency (%)
U83861
50.0%
K83861
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII838610
100.0%

Most frequent character per block

ValueCountFrequency (%)
995328
11.4%
189503
10.7%
U83861
10.0%
K83861
10.0%
-83861
10.0%
865271
7.8%
055684
6.6%
251289
 
6.1%
348646
 
5.8%
747562
 
5.7%
Other values (3)133744
15.9%

make_cons
Categorical

HIGH CORRELATION

Distinct44
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size655.3 KiB
Ford
13637 
BMW
9572 
Volkswagen
9123 
Opel
7609 
Audi
6681 
Other values (39)
37239 

Length

Max length13
Median length5
Mean length6.035666162
Min length2

Characters and Unicode

Total characters506157
Distinct characters46
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowPeugeot
2nd rowPeugeot
3rd rowBMW
4th rowBMW
5th rowNissan
ValueCountFrequency (%)
Ford13637
16.3%
BMW9572
11.4%
Volkswagen9123
10.9%
Opel7609
9.1%
Audi6681
 
8.0%
Mercedes Benz6115
 
7.3%
Renault5138
 
6.1%
Toyota3222
 
3.8%
Nissan3029
 
3.6%
Peugeot3007
 
3.6%
Other values (34)16728
19.9%
2022-05-03T12:07:45.428704image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ford13637
14.9%
bmw9572
10.4%
volkswagen9123
9.9%
opel7609
 
8.3%
audi6681
 
7.3%
mercedes6115
 
6.7%
benz6115
 
6.7%
renault5138
 
5.6%
toyota3222
 
3.5%
nissan3029
 
3.3%
Other values (37)21484
23.4%

Most occurring characters

ValueCountFrequency (%)
e59361
 
11.7%
o43529
 
8.6%
a31607
 
6.2%
d31603
 
6.2%
n29223
 
5.8%
r24641
 
4.9%
l24552
 
4.9%
s24199
 
4.8%
i18467
 
3.6%
M18344
 
3.6%
Other values (36)200631
39.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter387272
76.5%
Uppercase Letter110985
 
21.9%
Space Separator7864
 
1.6%
Other Punctuation36
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
e59361
15.3%
o43529
11.2%
a31607
 
8.2%
d31603
 
8.2%
n29223
 
7.5%
r24641
 
6.4%
l24552
 
6.3%
s24199
 
6.2%
i18467
 
4.8%
u18008
 
4.6%
Other values (14)82082
21.2%
ValueCountFrequency (%)
M18344
16.5%
B15687
14.1%
F14015
12.6%
V11345
10.2%
W9572
8.6%
O7609
6.9%
A6905
 
6.2%
R6887
 
6.2%
S3470
 
3.1%
T3440
 
3.1%
Other values (9)13711
12.4%
ValueCountFrequency (%)
/27
75.0%
.9
 
25.0%
ValueCountFrequency (%)
7864
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin498257
98.4%
Common7900
 
1.6%

Most frequent character per script

ValueCountFrequency (%)
e59361
 
11.9%
o43529
 
8.7%
a31607
 
6.3%
d31603
 
6.3%
n29223
 
5.9%
r24641
 
4.9%
l24552
 
4.9%
s24199
 
4.9%
i18467
 
3.7%
M18344
 
3.7%
Other values (33)192731
38.7%
ValueCountFrequency (%)
7864
99.5%
/27
 
0.3%
.9
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII506157
100.0%

Most frequent character per block

ValueCountFrequency (%)
e59361
 
11.7%
o43529
 
8.6%
a31607
 
6.2%
d31603
 
6.2%
n29223
 
5.8%
r24641
 
4.9%
l24552
 
4.9%
s24199
 
4.8%
i18467
 
3.6%
M18344
 
3.6%
Other values (36)200631
39.6%

registration_date
Categorical

HIGH CARDINALITY

Distinct4215
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size655.3 KiB
2018-03-31
 
556
2017-03-31
 
460
2018-04-30
 
398
2015-09-30
 
324
2016-03-31
 
299
Other values (4210)
81824 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters838610
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique431 ?
Unique (%)0.5%

Sample

1st row2013-12-31
2nd row2013-12-31
3rd row2016-03-31
4th row2016-03-31
5th row2016-07-31
ValueCountFrequency (%)
2018-03-31556
 
0.7%
2017-03-31460
 
0.5%
2018-04-30398
 
0.5%
2015-09-30324
 
0.4%
2016-03-31299
 
0.4%
2016-06-30248
 
0.3%
2016-05-31215
 
0.3%
2017-06-30202
 
0.2%
2014-09-30195
 
0.2%
2014-05-28185
 
0.2%
Other values (4205)80779
96.3%
2022-05-03T12:07:45.647407image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018-03-31556
 
0.7%
2017-03-31460
 
0.5%
2018-04-30398
 
0.5%
2015-09-30324
 
0.4%
2016-03-31299
 
0.4%
2016-06-30248
 
0.3%
2016-05-31215
 
0.3%
2017-06-30202
 
0.2%
2014-09-30195
 
0.2%
2014-05-28185
 
0.2%
Other values (4205)80779
96.3%

Most occurring characters

ValueCountFrequency (%)
0201070
24.0%
-167722
20.0%
1155062
18.5%
2137988
16.5%
337607
 
4.5%
625271
 
3.0%
524444
 
2.9%
923345
 
2.8%
723148
 
2.8%
421807
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number670888
80.0%
Dash Punctuation167722
 
20.0%

Most frequent character per category

ValueCountFrequency (%)
0201070
30.0%
1155062
23.1%
2137988
20.6%
337607
 
5.6%
625271
 
3.8%
524444
 
3.6%
923345
 
3.5%
723148
 
3.5%
421807
 
3.3%
821146
 
3.2%
ValueCountFrequency (%)
-167722
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common838610
100.0%

Most frequent character per script

ValueCountFrequency (%)
0201070
24.0%
-167722
20.0%
1155062
18.5%
2137988
16.5%
337607
 
4.5%
625271
 
3.0%
524444
 
2.9%
923345
 
2.8%
723148
 
2.8%
421807
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII838610
100.0%

Most frequent character per block

ValueCountFrequency (%)
0201070
24.0%
-167722
20.0%
1155062
18.5%
2137988
16.5%
337607
 
4.5%
625271
 
3.0%
524444
 
2.9%
923345
 
2.8%
723148
 
2.8%
421807
 
2.6%

model_cons
Categorical

HIGH CARDINALITY

Distinct433
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size655.3 KiB
TRANSIT
 
3862
3 SERIES
 
3796
GOLF
 
3752
FOCUS
 
2551
CORSA
 
2288
Other values (428)
67612 

Length

Max length24
Median length6
Mean length6.404669632
Min length2

Characters and Unicode

Total characters537102
Distinct characters53
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)0.1%

Sample

1st rowBIPPER
2nd rowBIPPER
3rd row4 SERIES
4th row4 SERIES
5th rowNP300
ValueCountFrequency (%)
TRANSIT3862
 
4.6%
3 SERIES3796
 
4.5%
GOLF3752
 
4.5%
FOCUS2551
 
3.0%
CORSA2288
 
2.7%
A32232
 
2.7%
PASSAT2138
 
2.5%
TRANSIT CUSTOM2055
 
2.5%
5 SERIES2010
 
2.4%
A41894
 
2.3%
Other values (423)57283
68.3%
2022-05-03T12:07:45.878610image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
series8191
 
8.1%
transit7519
 
7.5%
33796
 
3.8%
golf3756
 
3.7%
focus2551
 
2.5%
passat2478
 
2.5%
corsa2288
 
2.3%
a32232
 
2.2%
custom2133
 
2.1%
52010
 
2.0%
Other values (423)63741
63.3%

Most occurring characters

ValueCountFrequency (%)
S61402
11.4%
A59698
11.1%
R44043
 
8.2%
E41246
 
7.7%
I38429
 
7.2%
T37562
 
7.0%
O32036
 
6.0%
N28701
 
5.3%
C28270
 
5.3%
16834
 
3.1%
Other values (43)148881
27.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter481655
89.7%
Decimal Number30061
 
5.6%
Space Separator16834
 
3.1%
Dash Punctuation6354
 
1.2%
Other Punctuation1154
 
0.2%
Lowercase Letter868
 
0.2%
Math Symbol172
 
< 0.1%
Open Punctuation2
 
< 0.1%
Close Punctuation2
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
S61402
12.7%
A59698
12.4%
R44043
9.1%
E41246
8.6%
I38429
8.0%
T37562
7.8%
O32036
 
6.7%
N28701
 
6.0%
C28270
 
5.9%
L16377
 
3.4%
Other values (16)93891
19.5%
ValueCountFrequency (%)
37806
26.0%
05820
19.4%
54166
13.9%
43570
11.9%
12533
 
8.4%
62312
 
7.7%
21955
 
6.5%
81219
 
4.1%
7447
 
1.5%
9233
 
0.8%
ValueCountFrequency (%)
o186
21.4%
t124
14.3%
d124
14.3%
c62
 
7.1%
n62
 
7.1%
s62
 
7.1%
l62
 
7.1%
i62
 
7.1%
a62
 
7.1%
e62
 
7.1%
ValueCountFrequency (%)
/1108
96.0%
'46
 
4.0%
ValueCountFrequency (%)
16834
100.0%
ValueCountFrequency (%)
-6354
100.0%
ValueCountFrequency (%)
+172
100.0%
ValueCountFrequency (%)
(2
100.0%
ValueCountFrequency (%)
)2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin482523
89.8%
Common54579
 
10.2%

Most frequent character per script

ValueCountFrequency (%)
S61402
12.7%
A59698
12.4%
R44043
9.1%
E41246
8.5%
I38429
8.0%
T37562
 
7.8%
O32036
 
6.6%
N28701
 
5.9%
C28270
 
5.9%
L16377
 
3.4%
Other values (26)94759
19.6%
ValueCountFrequency (%)
16834
30.8%
37806
14.3%
-6354
 
11.6%
05820
 
10.7%
54166
 
7.6%
43570
 
6.5%
12533
 
4.6%
62312
 
4.2%
21955
 
3.6%
81219
 
2.2%
Other values (7)2010
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII537102
100.0%

Most frequent character per block

ValueCountFrequency (%)
S61402
11.4%
A59698
11.1%
R44043
 
8.2%
E41246
 
7.7%
I38429
 
7.2%
T37562
 
7.0%
O32036
 
6.0%
N28701
 
5.3%
C28270
 
5.3%
16834
 
3.1%
Other values (43)148881
27.7%

model_type_desc
Categorical

HIGH CARDINALITY

Distinct8914
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Memory size655.3 KiB
1.3D LIFE
 
1041
M SPORT
 
579
1.0T140 ST-LNNV
 
534
SPORT
 
474
SE
 
463
Other values (8909)
80770 

Length

Max length15
Median length13
Mean length11.85089613
Min length1

Characters and Unicode

Total characters993828
Distinct characters70
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2909 ?
Unique (%)3.5%

Sample

1st row1.3D75 PROF
2nd row1.3D75 PROF
3rd row2.0 M SPT PRF A
4th row2.0 M SPT PRF A
5th row190 D/C TEKNA A
ValueCountFrequency (%)
1.3D LIFE1041
 
1.2%
M SPORT579
 
0.7%
1.0T140 ST-LNNV534
 
0.6%
SPORT474
 
0.6%
SE463
 
0.6%
2.0d SE443
 
0.5%
M SPORT St433
 
0.5%
2.2D100L418
 
0.5%
2.2D85L408
 
0.5%
2.0d EFFCT DYNS406
 
0.5%
Other values (8904)78662
93.8%
2022-05-03T12:07:46.128888image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
se12532
 
5.9%
2.0d8220
 
3.9%
1.6d6718
 
3.2%
sport6707
 
3.2%
m3358
 
1.6%
nav2911
 
1.4%
bus2746
 
1.3%
au2703
 
1.3%
2.02590
 
1.2%
1.5d2488
 
1.2%
Other values (3026)160429
75.9%

Most occurring characters

ValueCountFrequency (%)
127829
 
12.9%
.64425
 
6.5%
163601
 
6.4%
E54072
 
5.4%
253626
 
5.4%
D53418
 
5.4%
T52837
 
5.3%
052473
 
5.3%
S50517
 
5.1%
L28254
 
2.8%
Other values (60)392776
39.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter488154
49.1%
Decimal Number252640
25.4%
Space Separator127829
 
12.9%
Other Punctuation71463
 
7.2%
Lowercase Letter46009
 
4.6%
Dash Punctuation4945
 
0.5%
Math Symbol2592
 
0.3%
Open Punctuation98
 
< 0.1%
Close Punctuation98
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
E54072
11.1%
D53418
 
10.9%
T52837
 
10.8%
S50517
 
10.3%
L28254
 
5.8%
N27520
 
5.6%
R23736
 
4.9%
C22273
 
4.6%
P20673
 
4.2%
A20081
 
4.1%
Other values (16)134773
27.6%
ValueCountFrequency (%)
d18873
41.0%
i6060
 
13.2%
e3379
 
7.3%
t3106
 
6.8%
s2915
 
6.3%
p1953
 
4.2%
f1919
 
4.2%
h1296
 
2.8%
n1053
 
2.3%
y927
 
2.0%
Other values (14)4528
 
9.8%
ValueCountFrequency (%)
163601
25.2%
253626
21.2%
052473
20.8%
522133
 
8.8%
615569
 
6.2%
314425
 
5.7%
411385
 
4.5%
77325
 
2.9%
96530
 
2.6%
85573
 
2.2%
ValueCountFrequency (%)
.64425
90.2%
/6902
 
9.7%
&77
 
0.1%
"53
 
0.1%
\6
 
< 0.1%
ValueCountFrequency (%)
127829
100.0%
ValueCountFrequency (%)
-4945
100.0%
ValueCountFrequency (%)
+2592
100.0%
ValueCountFrequency (%)
(98
100.0%
ValueCountFrequency (%)
)98
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin534163
53.7%
Common459665
46.3%

Most frequent character per script

ValueCountFrequency (%)
E54072
 
10.1%
D53418
 
10.0%
T52837
 
9.9%
S50517
 
9.5%
L28254
 
5.3%
N27520
 
5.2%
R23736
 
4.4%
C22273
 
4.2%
P20673
 
3.9%
A20081
 
3.8%
Other values (40)180782
33.8%
ValueCountFrequency (%)
127829
27.8%
.64425
14.0%
163601
13.8%
253626
11.7%
052473
11.4%
522133
 
4.8%
615569
 
3.4%
314425
 
3.1%
411385
 
2.5%
77325
 
1.6%
Other values (10)26874
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII993828
100.0%

Most frequent character per block

ValueCountFrequency (%)
127829
 
12.9%
.64425
 
6.5%
163601
 
6.4%
E54072
 
5.4%
253626
 
5.4%
D53418
 
5.4%
T52837
 
5.3%
052473
 
5.3%
S50517
 
5.1%
L28254
 
2.8%
Other values (60)392776
39.5%

manufacturer_group
Categorical

HIGH CORRELATION

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size655.3 KiB
VAG
19133 
Ford Motor Co.
13637 
BMW Group
10281 
Renault-Nissan
8202 
GM
7709 
Other values (17)
24899 

Length

Max length31
Median length9
Mean length8.495248089
Min length2

Characters and Unicode

Total characters712420
Distinct characters39
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPSA
2nd rowPSA
3rd rowBMW Group
4th rowBMW Group
5th rowRenault-Nissan
ValueCountFrequency (%)
VAG19133
22.8%
Ford Motor Co.13637
16.3%
BMW Group10281
12.3%
Renault-Nissan8202
9.8%
GM7709
9.2%
Daimler AG6142
 
7.3%
PSA5422
 
6.5%
Toyota Motor Co3704
 
4.4%
Tata Group2383
 
2.8%
Geely Group2222
 
2.6%
Other values (12)5026
 
6.0%
2022-05-03T12:07:46.316340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
vag19133
13.2%
motor18765
13.0%
co17903
12.4%
group16852
11.6%
ford13637
9.4%
bmw10281
7.1%
renault-nissan8202
5.7%
gm7709
 
5.3%
ag6142
 
4.2%
daimler6142
 
4.2%
Other values (20)20074
13.9%

Most occurring characters

ValueCountFrequency (%)
o95386
 
13.4%
60979
 
8.6%
r56684
 
8.0%
G52058
 
7.3%
M38888
 
5.5%
a35839
 
5.0%
t35367
 
5.0%
A30858
 
4.3%
u26795
 
3.8%
i19828
 
2.8%
Other values (29)259738
36.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter392144
55.0%
Uppercase Letter236729
33.2%
Space Separator60979
 
8.6%
Other Punctuation14208
 
2.0%
Dash Punctuation8202
 
1.2%
Open Punctuation79
 
< 0.1%
Close Punctuation79
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
o95386
24.3%
r56684
14.5%
a35839
 
9.1%
t35367
 
9.0%
u26795
 
6.8%
i19828
 
5.1%
e19202
 
4.9%
s19136
 
4.9%
p17714
 
4.5%
n17554
 
4.5%
Other values (8)48639
12.4%
ValueCountFrequency (%)
G52058
22.0%
M38888
16.4%
A30858
13.0%
V19133
 
8.1%
C18923
 
8.0%
F14682
 
6.2%
B10281
 
4.3%
W10281
 
4.3%
N8205
 
3.5%
R8202
 
3.5%
Other values (6)25218
10.7%
ValueCountFrequency (%)
60979
100.0%
ValueCountFrequency (%)
-8202
100.0%
ValueCountFrequency (%)
.14208
100.0%
ValueCountFrequency (%)
(79
100.0%
ValueCountFrequency (%)
)79
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin628873
88.3%
Common83547
 
11.7%

Most frequent character per script

ValueCountFrequency (%)
o95386
15.2%
r56684
 
9.0%
G52058
 
8.3%
M38888
 
6.2%
a35839
 
5.7%
t35367
 
5.6%
A30858
 
4.9%
u26795
 
4.3%
i19828
 
3.2%
e19202
 
3.1%
Other values (24)217968
34.7%
ValueCountFrequency (%)
60979
73.0%
.14208
 
17.0%
-8202
 
9.8%
(79
 
0.1%
)79
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII712420
100.0%

Most frequent character per block

ValueCountFrequency (%)
o95386
 
13.4%
60979
 
8.6%
r56684
 
8.0%
G52058
 
7.3%
M38888
 
5.5%
a35839
 
5.0%
t35367
 
5.0%
A30858
 
4.3%
u26795
 
3.8%
i19828
 
2.8%
Other values (29)259738
36.5%

vehicle_type_cons
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size655.3 KiB
Car
60943 
Van
22918 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters251583
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVan
2nd rowVan
3rd rowCar
4th rowCar
5th rowVan
ValueCountFrequency (%)
Car60943
72.7%
Van22918
 
27.3%
2022-05-03T12:07:46.488175image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2022-05-03T12:07:46.550660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
car60943
72.7%
van22918
 
27.3%

Most occurring characters

ValueCountFrequency (%)
a83861
33.3%
C60943
24.2%
r60943
24.2%
V22918
 
9.1%
n22918
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter167722
66.7%
Uppercase Letter83861
33.3%

Most frequent character per category

ValueCountFrequency (%)
a83861
50.0%
r60943
36.3%
n22918
 
13.7%
ValueCountFrequency (%)
C60943
72.7%
V22918
 
27.3%

Most occurring scripts

ValueCountFrequency (%)
Latin251583
100.0%

Most frequent character per script

ValueCountFrequency (%)
a83861
33.3%
C60943
24.2%
r60943
24.2%
V22918
 
9.1%
n22918
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII251583
100.0%

Most frequent character per block

ValueCountFrequency (%)
a83861
33.3%
C60943
24.2%
r60943
24.2%
V22918
 
9.1%
n22918
 
9.1%

vehicle_segment_desc
Categorical

HIGH CORRELATION

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size655.3 KiB
Compact car
18555 
Medium car
17354 
Commercial van
14582 
SUV
10714 
Small car
7148 
Other values (11)
15508 

Length

Max length22
Median length11
Mean length10.51235974
Min length3

Characters and Unicode

Total characters881577
Distinct characters31
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCar derived van
2nd rowCar derived van
3rd rowMedium car
4th rowMedium car
5th rowOther Van
ValueCountFrequency (%)
Compact car18555
22.1%
Medium car17354
20.7%
Commercial van14582
17.4%
SUV10714
12.8%
Small car7148
 
8.5%
Car derived van5417
 
6.5%
Executive car5038
 
6.0%
Other Van1685
 
2.0%
Not consolidated872
 
1.0%
Other Car786
 
0.9%
Other values (6)1710
 
2.0%
2022-05-03T12:07:46.723675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
car55100
33.9%
van21767
 
13.4%
compact18555
 
11.4%
medium17354
 
10.7%
commercial15320
 
9.4%
suv10714
 
6.6%
small7148
 
4.4%
derived6155
 
3.8%
executive5038
 
3.1%
other2471
 
1.5%
Other values (6)2807
 
1.7%

Most occurring characters

ValueCountFrequency (%)
a118853
13.5%
c88674
 
10.1%
r79102
 
9.0%
78568
 
8.9%
m73697
 
8.4%
e58403
 
6.6%
i44739
 
5.1%
C40086
 
4.5%
o36652
 
4.2%
d31408
 
3.6%
Other values (21)231395
26.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter693648
78.7%
Uppercase Letter109361
 
12.4%
Space Separator78568
 
8.9%

Most frequent character per category

ValueCountFrequency (%)
a118853
17.1%
c88674
12.8%
r79102
11.4%
m73697
10.6%
e58403
8.4%
i44739
 
6.4%
o36652
 
5.3%
d31408
 
4.5%
v31192
 
4.5%
l30488
 
4.4%
Other values (10)100440
14.5%
ValueCountFrequency (%)
C40086
36.7%
M18109
16.6%
S17862
16.3%
V13237
 
12.1%
U10784
 
9.9%
E5038
 
4.6%
O2471
 
2.3%
N963
 
0.9%
P755
 
0.7%
L56
 
0.1%
ValueCountFrequency (%)
78568
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin803009
91.1%
Common78568
 
8.9%

Most frequent character per script

ValueCountFrequency (%)
a118853
14.8%
c88674
11.0%
r79102
 
9.9%
m73697
 
9.2%
e58403
 
7.3%
i44739
 
5.6%
C40086
 
5.0%
o36652
 
4.6%
d31408
 
3.9%
v31192
 
3.9%
Other values (20)200203
24.9%
ValueCountFrequency (%)
78568
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII881577
100.0%

Most frequent character per block

ValueCountFrequency (%)
a118853
13.5%
c88674
 
10.1%
r79102
 
9.0%
78568
 
8.9%
m73697
 
8.4%
e58403
 
6.6%
i44739
 
5.1%
C40086
 
4.5%
o36652
 
4.2%
d31408
 
3.6%
Other values (21)231395
26.2%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size655.3 KiB
Diesel
68612 
Petrol
11704 
Electric
 
3494
LPG
 
51

Length

Max length8
Median length6
Mean length6.081503917
Min length3

Characters and Unicode

Total characters510001
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDiesel
2nd rowDiesel
3rd rowDiesel
4th rowDiesel
5th rowDiesel
ValueCountFrequency (%)
Diesel68612
81.8%
Petrol11704
 
14.0%
Electric3494
 
4.2%
LPG51
 
0.1%
2022-05-03T12:07:46.911131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2022-05-03T12:07:46.957998image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
diesel68612
81.8%
petrol11704
 
14.0%
electric3494
 
4.2%
lpg51
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e152422
29.9%
l83810
16.4%
i72106
14.1%
D68612
13.5%
s68612
13.5%
t15198
 
3.0%
r15198
 
3.0%
P11755
 
2.3%
o11704
 
2.3%
c6988
 
1.4%
Other values (3)3596
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter426038
83.5%
Uppercase Letter83963
 
16.5%

Most frequent character per category

ValueCountFrequency (%)
e152422
35.8%
l83810
19.7%
i72106
16.9%
s68612
16.1%
t15198
 
3.6%
r15198
 
3.6%
o11704
 
2.7%
c6988
 
1.6%
ValueCountFrequency (%)
D68612
81.7%
P11755
 
14.0%
E3494
 
4.2%
L51
 
0.1%
G51
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin510001
100.0%

Most frequent character per script

ValueCountFrequency (%)
e152422
29.9%
l83810
16.4%
i72106
14.1%
D68612
13.5%
s68612
13.5%
t15198
 
3.0%
r15198
 
3.0%
P11755
 
2.3%
o11704
 
2.3%
c6988
 
1.4%
Other values (3)3596
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII510001
100.0%

Most frequent character per block

ValueCountFrequency (%)
e152422
29.9%
l83810
16.4%
i72106
14.1%
D68612
13.5%
s68612
13.5%
t15198
 
3.0%
r15198
 
3.0%
P11755
 
2.3%
o11704
 
2.3%
c6988
 
1.4%
Other values (3)3596
 
0.7%

fuel_consump_manufacturer
Real number (ℝ≥0)

ZEROS

Distinct176
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.005546082
Minimum0
Maximum100
Zeros49765
Zeros (%)59.3%
Memory size655.3 KiB
2022-05-03T12:07:47.051725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.56
95-th percentile2.34
Maximum100
Range100
Interquartile range (IQR)1.56

Descriptive statistics

Standard deviation5.516054246
Coefficient of variation (CV)5.485630489
Kurtosis308.7688061
Mean1.005546082
Median Absolute Deviation (MAD)0
Skewness17.37494842
Sum84326.1
Variance30.42685445
MonotocityNot monotonic
2022-05-03T12:07:47.161072image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
049765
59.3%
1.522012
 
2.4%
1.451479
 
1.8%
1.591476
 
1.8%
1.351303
 
1.6%
1.491265
 
1.5%
1.421240
 
1.5%
1.661170
 
1.4%
1.841095
 
1.3%
1.561063
 
1.3%
Other values (166)21993
26.2%
ValueCountFrequency (%)
049765
59.3%
0.2123
 
< 0.1%
0.256
 
< 0.1%
0.3910
 
< 0.1%
0.428
 
< 0.1%
ValueCountFrequency (%)
100252
0.3%
17.2420
 
< 0.1%
4.521
 
< 0.1%
4.352
 
< 0.1%
4.221
 
< 0.1%

cat_price_euro
Real number (ℝ≥0)

Distinct10247
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23824.026
Minimum1.11
Maximum120815.27
Zeros0
Zeros (%)0.0%
Memory size655.3 KiB
2022-05-03T12:07:47.270422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.11
5-th percentile12479.92
Q118775.47
median22726.98
Q328295.23
95-th percentile36564.22
Maximum120815.27
Range120814.16
Interquartile range (IQR)9519.76

Descriptive statistics

Standard deviation7937.687993
Coefficient of variation (CV)0.3331799584
Kurtosis5.388476221
Mean23824.026
Median Absolute Deviation (MAD)4609.32
Skewness1.251400156
Sum1997906645
Variance63006890.67
MonotocityNot monotonic
2022-05-03T12:07:47.395392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9369.361034
 
1.2%
19979.91546
 
0.7%
21229.76312
 
0.4%
25126.61301
 
0.4%
19906.72279
 
0.3%
9714.73209
 
0.2%
14953.66208
 
0.2%
26677.56195
 
0.2%
24248.29194
 
0.2%
38990.72182
 
0.2%
Other values (10237)80401
95.9%
ValueCountFrequency (%)
1.1111
< 0.1%
5959.231
 
< 0.1%
6198.773
 
< 0.1%
6429.881
 
< 0.1%
6572.572
 
< 0.1%
ValueCountFrequency (%)
120815.271
 
< 0.1%
107293.042
< 0.1%
102895.632
< 0.1%
99575.311
 
< 0.1%
96179.73
< 0.1%

cat_price_options_euro
Real number (ℝ)

ZEROS

Distinct18360
Distinct (%)21.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean503.3524755
Minimum-137.86
Maximum18942.18
Zeros37607
Zeros (%)44.8%
Memory size655.3 KiB
2022-05-03T12:07:47.504741image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-137.86
5-th percentile0
Q10
median178.83
Q3576.46
95-th percentile2165.7
Maximum18942.18
Range19080.04
Interquartile range (IQR)576.46

Descriptive statistics

Standard deviation900.8711171
Coefficient of variation (CV)1.789742101
Kurtosis37.93733427
Mean503.3524755
Median Absolute Deviation (MAD)178.83
Skewness4.481621141
Sum42211641.95
Variance811568.7697
MonotocityNot monotonic
2022-05-03T12:07:47.614085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
037607
44.8%
171.08603
 
0.7%
486.41260
 
0.3%
478.07225
 
0.3%
458.61221
 
0.3%
111.17211
 
0.3%
417.34196
 
0.2%
437.21174
 
0.2%
500.3173
 
0.2%
504.94156
 
0.2%
Other values (18350)44035
52.5%
ValueCountFrequency (%)
-137.861
 
< 0.1%
-49.753
 
< 0.1%
037607
44.8%
0.371
 
< 0.1%
2.831
 
< 0.1%
ValueCountFrequency (%)
18942.181
< 0.1%
183782
< 0.1%
16468.492
< 0.1%
16167.391
< 0.1%
16121.051
< 0.1%

cat_price_accessories_euro
Real number (ℝ≥0)

ZEROS

Distinct4463
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean546.7760669
Minimum0
Maximum86587.86
Zeros64618
Zeros (%)77.1%
Memory size655.3 KiB
2022-05-03T12:07:47.724716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3342.07
Maximum86587.86
Range86587.86
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2096.997024
Coefficient of variation (CV)3.835202656
Kurtosis309.0504976
Mean546.7760669
Median Absolute Deviation (MAD)0
Skewness11.69753734
Sum45853187.75
Variance4397396.52
MonotocityNot monotonic
2022-05-03T12:07:47.834060image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
064618
77.1%
157.87644
 
0.8%
360.72409
 
0.5%
189385
 
0.5%
5.83316
 
0.4%
118.29254
 
0.3%
203.45240
 
0.3%
2473.17198
 
0.2%
222.35129
 
0.2%
753.98125
 
0.1%
Other values (4453)16543
 
19.7%
ValueCountFrequency (%)
064618
77.1%
0.0117
 
< 0.1%
0.021
 
< 0.1%
0.881
 
< 0.1%
1.1127
 
< 0.1%
ValueCountFrequency (%)
86587.862
< 0.1%
84364.263
< 0.1%
83697.193
< 0.1%
57505.471
 
< 0.1%
46289.663
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size655.3 KiB
Full service Closed calculation
43094 
Open Calculation
40705 
Finance Only
 
62

Length

Max length31
Median length31
Mean length23.70515496
Min length12

Characters and Unicode

Total characters1987938
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFull service Closed calculation
2nd rowFull service Closed calculation
3rd rowFull service Closed calculation
4th rowFull service Closed calculation
5th rowFull service Closed calculation
ValueCountFrequency (%)
Full service Closed calculation43094
51.4%
Open Calculation40705
48.5%
Finance Only62
 
0.1%
2022-05-03T12:07:48.412051image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2022-05-03T12:07:48.474539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
calculation83799
33.0%
closed43094
17.0%
full43094
17.0%
service43094
17.0%
open40705
16.0%
finance62
 
< 0.1%
only62
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
l296942
14.9%
170049
8.6%
e170049
8.6%
c170049
8.6%
a167660
 
8.4%
i126955
 
6.4%
u126893
 
6.4%
o126893
 
6.4%
n124690
 
6.3%
s86188
 
4.3%
Other values (9)421570
21.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1650167
83.0%
Space Separator170049
 
8.6%
Uppercase Letter167722
 
8.4%

Most frequent character per category

ValueCountFrequency (%)
l296942
18.0%
e170049
10.3%
c170049
10.3%
a167660
10.2%
i126955
7.7%
u126893
7.7%
o126893
7.7%
n124690
7.6%
s86188
 
5.2%
t83799
 
5.1%
Other values (5)170049
10.3%
ValueCountFrequency (%)
C83799
50.0%
F43156
25.7%
O40767
24.3%
ValueCountFrequency (%)
170049
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1817889
91.4%
Common170049
 
8.6%

Most frequent character per script

ValueCountFrequency (%)
l296942
16.3%
e170049
9.4%
c170049
9.4%
a167660
9.2%
i126955
 
7.0%
u126893
 
7.0%
o126893
 
7.0%
n124690
 
6.9%
s86188
 
4.7%
C83799
 
4.6%
Other values (8)337771
18.6%
ValueCountFrequency (%)
170049
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1987938
100.0%

Most frequent character per block

ValueCountFrequency (%)
l296942
14.9%
170049
8.6%
e170049
8.6%
c170049
8.6%
a167660
 
8.4%
i126955
 
6.4%
u126893
 
6.4%
o126893
 
6.4%
n124690
 
6.3%
s86188
 
4.3%
Other values (9)421570
21.2%

engine_capacity
Real number (ℝ≥0)

Distinct215
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1831.290087
Minimum1
Maximum5513
Zeros0
Zeros (%)0.0%
Memory size655.3 KiB
2022-05-03T12:07:48.552643image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1229
Q11560
median1968
Q31998
95-th percentile2402
Maximum5513
Range5512
Interquartile range (IQR)438

Descriptive statistics

Standard deviation423.2748694
Coefficient of variation (CV)0.2311348007
Kurtosis4.236574638
Mean1831.290087
Median Absolute Deviation (MAD)230
Skewness-0.4251176918
Sum153573818
Variance179161.615
MonotocityNot monotonic
2022-05-03T12:07:48.663234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
196810498
 
12.5%
15989272
 
11.1%
19957555
 
9.0%
15604951
 
5.9%
21984623
 
5.5%
14613733
 
4.5%
21433660
 
4.4%
19982565
 
3.1%
19972348
 
2.8%
12482295
 
2.7%
Other values (205)32361
38.6%
ValueCountFrequency (%)
1908
1.1%
6982
 
< 0.1%
7992
 
< 0.1%
87515
 
< 0.1%
89850
 
0.1%
ValueCountFrequency (%)
55132
 
< 0.1%
54613
 
< 0.1%
50005
 
< 0.1%
46636
< 0.1%
436713
< 0.1%

engine_power
Real number (ℝ≥0)

Distinct198
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.4672017
Minimum0
Maximum508
Zeros38
Zeros (%)< 0.1%
Memory size655.3 KiB
2022-05-03T12:07:48.772616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile55
Q180
median96
Q3120
95-th percentile163
Maximum508
Range508
Interquartile range (IQR)40

Descriptive statistics

Standard deviation35.25534416
Coefficient of variation (CV)0.3474555677
Kurtosis7.362280019
Mean101.4672017
Median Absolute Deviation (MAD)19
Skewness1.833430443
Sum8509141
Variance1242.939292
MonotocityNot monotonic
2022-05-03T12:07:48.881965image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1106414
 
7.6%
1034459
 
5.3%
774323
 
5.2%
814255
 
5.1%
854071
 
4.9%
553769
 
4.5%
1003607
 
4.3%
1403305
 
3.9%
663156
 
3.8%
963122
 
3.7%
Other values (188)43380
51.7%
ValueCountFrequency (%)
038
 
< 0.1%
4057
 
0.1%
4110
 
< 0.1%
432
 
< 0.1%
44231
0.3%
ValueCountFrequency (%)
5081
 
< 0.1%
4513
 
< 0.1%
44913
< 0.1%
4302
 
< 0.1%
4051
 
< 0.1%

gross_weight
Real number (ℝ≥0)

ZEROS

Distinct695
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2199.739104
Minimum0
Maximum7000
Zeros1980
Zeros (%)2.4%
Memory size655.3 KiB
2022-05-03T12:07:48.993555image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1550
Q11885
median2090
Q32390
95-th percentile3500
Maximum7000
Range7000
Interquartile range (IQR)505

Descriptive statistics

Standard deviation649.801155
Coefficient of variation (CV)0.2953991925
Kurtosis4.009477591
Mean2199.739104
Median Absolute Deviation (MAD)230
Skewness0.2100873553
Sum184472321
Variance422241.5411
MonotocityNot monotonic
2022-05-03T12:07:49.102903image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35005388
 
6.4%
19002450
 
2.9%
01980
 
2.4%
21001548
 
1.8%
28001288
 
1.5%
16751169
 
1.4%
20201045
 
1.2%
18501032
 
1.2%
1940986
 
1.2%
1805962
 
1.1%
Other values (685)66013
78.7%
ValueCountFrequency (%)
01980
2.4%
9802
 
< 0.1%
10205
 
< 0.1%
10505
 
< 0.1%
11501
 
< 0.1%
ValueCountFrequency (%)
700021
 
< 0.1%
55005
 
< 0.1%
5200152
0.2%
5000109
0.1%
47006
 
< 0.1%

tare_weight
Real number (ℝ≥0)

ZEROS

Distinct1243
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1435.224336
Minimum0
Maximum3011
Zeros5642
Zeros (%)6.7%
Memory size655.3 KiB
2022-05-03T12:07:49.245726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11302
median1475
Q31699
95-th percentile2045
Maximum3011
Range3011
Interquartile range (IQR)397

Descriptive statistics

Standard deviation477.831333
Coefficient of variation (CV)0.3329314597
Kurtosis3.238187863
Mean1435.224336
Median Absolute Deviation (MAD)195
Skewness-1.437748204
Sum120359348
Variance228322.7828
MonotocityNot monotonic
2022-05-03T12:07:49.355094image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05642
 
6.7%
13201735
 
2.1%
14201549
 
1.8%
12801482
 
1.8%
11601217
 
1.5%
14301216
 
1.5%
1660857
 
1.0%
1845822
 
1.0%
1370784
 
0.9%
1540727
 
0.9%
Other values (1233)67830
80.9%
ValueCountFrequency (%)
05642
6.7%
6051
 
< 0.1%
7202
 
< 0.1%
7503
 
< 0.1%
7702
 
< 0.1%
ValueCountFrequency (%)
301130
< 0.1%
298421
< 0.1%
28322
 
< 0.1%
27831
 
< 0.1%
27664
 
< 0.1%

co2_level_combined
Real number (ℝ≥0)

ZEROS

Distinct255
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean125.8197493
Minimum0
Maximum999
Zeros3992
Zeros (%)4.8%
Memory size655.3 KiB
2022-05-03T12:07:49.477420image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile32
Q1107
median120
Q3149
95-th percentile209
Maximum999
Range999
Interquartile range (IQR)42

Descriptive statistics

Standard deviation47.26872164
Coefficient of variation (CV)0.3756860261
Kurtosis2.978972358
Mean125.8197493
Median Absolute Deviation (MAD)18
Skewness-0.1469358058
Sum10551370
Variance2234.332045
MonotocityNot monotonic
2022-05-03T12:07:49.586773image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1195454
 
6.5%
1094044
 
4.8%
03992
 
4.8%
993643
 
4.3%
1202401
 
2.9%
1292250
 
2.7%
1391882
 
2.2%
1141865
 
2.2%
1121827
 
2.2%
1041262
 
1.5%
Other values (245)55241
65.9%
ValueCountFrequency (%)
03992
4.8%
116
 
< 0.1%
123
 
< 0.1%
1326
 
< 0.1%
146
 
< 0.1%
ValueCountFrequency (%)
9991
 
< 0.1%
3482
 
< 0.1%
34313
< 0.1%
34013
< 0.1%
3279
< 0.1%

number_of_cylinders
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.823064356
Minimum0
Maximum12
Zeros3673
Zeros (%)4.4%
Memory size655.3 KiB
2022-05-03T12:07:49.681719image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q14
median4
Q34
95-th percentile4
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8994037623
Coefficient of variation (CV)0.2352572906
Kurtosis12.39768167
Mean3.823064356
Median Absolute Deviation (MAD)0
Skewness-3.049042464
Sum320606
Variance0.8089271276
MonotocityNot monotonic
2022-05-03T12:07:49.759859image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
473972
88.2%
33984
 
4.8%
03673
 
4.4%
61538
 
1.8%
5622
 
0.7%
844
 
0.1%
226
 
< 0.1%
122
 
< 0.1%
ValueCountFrequency (%)
03673
 
4.4%
226
 
< 0.1%
33984
 
4.8%
473972
88.2%
5622
 
0.7%
ValueCountFrequency (%)
122
 
< 0.1%
844
 
0.1%
61538
 
1.8%
5622
 
0.7%
473972
88.2%

number_of_doors
Real number (ℝ)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.464113235
Minimum-6
Maximum9
Zeros20982
Zeros (%)25.0%
Memory size655.3 KiB
2022-05-03T12:07:49.837968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-6
5-th percentile0
Q10
median5
Q35
95-th percentile5
Maximum9
Range15
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.1046983
Coefficient of variation (CV)0.607572027
Kurtosis-0.9636696509
Mean3.464113235
Median Absolute Deviation (MAD)0
Skewness-0.9149164142
Sum290504
Variance4.429754934
MonotocityNot monotonic
2022-05-03T12:07:49.900447image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
546549
55.5%
020982
25.0%
411246
 
13.4%
32576
 
3.1%
22480
 
3.0%
620
 
< 0.1%
-67
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
-67
 
< 0.1%
020982
25.0%
22480
 
3.0%
32576
 
3.1%
411246
13.4%
ValueCountFrequency (%)
91
 
< 0.1%
620
 
< 0.1%
546549
55.5%
411246
 
13.4%
32576
 
3.1%

contract_duration
Real number (ℝ≥0)

Distinct66
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.86346454
Minimum1
Maximum92
Zeros0
Zeros (%)0.0%
Memory size655.3 KiB
2022-05-03T12:07:49.994176image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12
Q124
median36
Q348
95-th percentile60
Maximum92
Range91
Interquartile range (IQR)24

Descriptive statistics

Standard deviation15.11114979
Coefficient of variation (CV)0.4213522029
Kurtosis-0.5077759514
Mean35.86346454
Median Absolute Deviation (MAD)12
Skewness-0.366571452
Sum3007546
Variance228.3468479
MonotocityNot monotonic
2022-05-03T12:07:50.103526image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3628519
34.0%
4823229
27.7%
1211934
14.2%
606886
 
8.2%
246296
 
7.5%
63431
 
4.1%
42522
 
0.6%
30382
 
0.5%
54185
 
0.2%
40180
 
0.2%
Other values (56)2297
 
2.7%
ValueCountFrequency (%)
131
< 0.1%
210
 
< 0.1%
35
 
< 0.1%
47
 
< 0.1%
515
< 0.1%
ValueCountFrequency (%)
921
 
< 0.1%
911
 
< 0.1%
84176
0.2%
7282
0.1%
691
 
< 0.1%

contract_mileage
Real number (ℝ≥0)

Distinct16481
Distinct (%)19.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54619.83407
Minimum500
Maximum270000
Zeros0
Zeros (%)0.0%
Memory size655.3 KiB
2022-05-03T12:07:50.212876image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile8000
Q125000
median52500
Q380000
95-th percentile120000
Maximum270000
Range269500
Interquartile range (IQR)55000

Descriptive statistics

Standard deviation34811.06138
Coefficient of variation (CV)0.637333708
Kurtosis0.1301257559
Mean54619.83407
Median Absolute Deviation (MAD)27500
Skewness0.6108718211
Sum4580473905
Variance1211809994
MonotocityNot monotonic
2022-05-03T12:07:50.322224image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600006943
 
8.3%
800005019
 
6.0%
300003678
 
4.4%
100003214
 
3.8%
750003053
 
3.6%
1000002484
 
3.0%
900002416
 
2.9%
400002086
 
2.5%
200002039
 
2.4%
450002033
 
2.4%
Other values (16471)50896
60.7%
ValueCountFrequency (%)
5002
< 0.1%
7501
< 0.1%
7561
< 0.1%
8961
< 0.1%
9721
< 0.1%
ValueCountFrequency (%)
2700002
 
< 0.1%
2500002
 
< 0.1%
2128951
 
< 0.1%
2017851
 
< 0.1%
2000007
< 0.1%

monthly_lease_installment
Real number (ℝ)

Distinct39553
Distinct (%)47.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean360.0503313
Minimum-500.07
Maximum2891.8
Zeros1
Zeros (%)< 0.1%
Memory size655.3 KiB
2022-05-03T12:07:50.447195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-500.07
5-th percentile137.22
Q1254.77
median338.79
Q3448.11
95-th percentile626.31
Maximum2891.8
Range3391.87
Interquartile range (IQR)193.34

Descriptive statistics

Standard deviation156.7400635
Coefficient of variation (CV)0.4353282026
Kurtosis5.711772439
Mean360.0503313
Median Absolute Deviation (MAD)94.86
Skewness1.298408366
Sum30194180.83
Variance24567.44752
MonotocityNot monotonic
2022-05-03T12:07:50.556544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
294.2159
 
0.2%
321.34121
 
0.1%
310.46105
 
0.1%
174.7360
 
0.1%
514.7657
 
0.1%
599.5552
 
0.1%
84.9851
 
0.1%
300.6849
 
0.1%
159.1149
 
0.1%
216.6448
 
0.1%
Other values (39543)83110
99.1%
ValueCountFrequency (%)
-500.071
< 0.1%
01
< 0.1%
12
< 0.1%
11.491
< 0.1%
15.851
< 0.1%
ValueCountFrequency (%)
2891.81
< 0.1%
2086.961
< 0.1%
2058.341
< 0.1%
2031.641
< 0.1%
2009.531
< 0.1%

supplier_discount_perc
Real number (ℝ)

ZEROS

Distinct2101
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.492942607
Minimum-2
Maximum57.97
Zeros44032
Zeros (%)52.5%
Memory size655.3 KiB
2022-05-03T12:07:50.667792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile0
Q10
median0
Q310.25
95-th percentile19
Maximum57.97
Range59.97
Interquartile range (IQR)10.25

Descriptive statistics

Standard deviation8.26226607
Coefficient of variation (CV)1.504160276
Kurtosis6.624982316
Mean5.492942607
Median Absolute Deviation (MAD)0
Skewness2.253848121
Sum460643.66
Variance68.26504062
MonotocityNot monotonic
2022-05-03T12:07:50.777174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
044032
52.5%
123320
 
4.0%
113246
 
3.9%
102772
 
3.3%
142545
 
3.0%
81869
 
2.2%
2.51756
 
2.1%
91614
 
1.9%
31519
 
1.8%
10.51455
 
1.7%
Other values (2091)19733
23.5%
ValueCountFrequency (%)
-28
 
< 0.1%
-0.41
 
< 0.1%
-0.231
 
< 0.1%
-0.081
 
< 0.1%
044032
52.5%
ValueCountFrequency (%)
57.971
 
< 0.1%
56.281
 
< 0.1%
55.596
< 0.1%
55.571
 
< 0.1%
55.552
 
< 0.1%

discount_to_customer
Real number (ℝ≥0)

ZEROS

Distinct32394
Distinct (%)38.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4037.132729
Minimum0
Maximum19007.49
Zeros17539
Zeros (%)20.9%
Memory size655.3 KiB
2022-05-03T12:07:50.902144image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11646.26
median3825.26
Q35871.29
95-th percentile9964.85
Maximum19007.49
Range19007.49
Interquartile range (IQR)4225.03

Descriptive statistics

Standard deviation3214.28036
Coefficient of variation (CV)0.7961790152
Kurtosis0.4251740871
Mean4037.132729
Median Absolute Deviation (MAD)2103.26
Skewness0.7107629435
Sum338557987.8
Variance10331598.23
MonotocityNot monotonic
2022-05-03T12:07:51.011497image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
017539
 
20.9%
6788.35256
 
0.3%
7342.03246
 
0.3%
4458.37170
 
0.2%
5805.81148
 
0.2%
8345.83124
 
0.1%
3500123
 
0.1%
12476.15116
 
0.1%
4278.56114
 
0.1%
8673.33103
 
0.1%
Other values (32384)64922
77.4%
ValueCountFrequency (%)
017539
20.9%
30.411
 
< 0.1%
56.831
 
< 0.1%
56.841
 
< 0.1%
62.191
 
< 0.1%
ValueCountFrequency (%)
19007.491
 
< 0.1%
18574.581
 
< 0.1%
180394
 
< 0.1%
16860.616
< 0.1%
168001
 
< 0.1%

enriched_make
Categorical

HIGH CORRELATION

Distinct44
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size655.3 KiB
Ford
13637 
Bmw
9572 
Volkswagen
9123 
Opel
7609 
Audi
6681 
Other values (39)
37239 

Length

Max length10
Median length5
Mean length5.669679589
Min length2

Characters and Unicode

Total characters475465
Distinct characters43
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowPeugeot
2nd rowPeugeot
3rd rowBmw
4th rowBmw
5th rowNissan
ValueCountFrequency (%)
Ford13637
16.3%
Bmw9572
11.4%
Volkswagen9123
10.9%
Opel7609
9.1%
Audi6681
 
8.0%
Mercedes6115
 
7.3%
Renault5138
 
6.1%
Toyota3222
 
3.8%
Nissan3029
 
3.6%
Peugeot3007
 
3.6%
Other values (34)16728
19.9%
2022-05-03T12:07:51.245822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ford13637
15.9%
bmw9572
11.2%
volkswagen9123
10.7%
opel7609
8.9%
audi6681
 
7.8%
mercedes6115
 
7.1%
renault5138
 
6.0%
toyota3222
 
3.8%
nissan3029
 
3.5%
peugeot3007
 
3.5%
Other values (36)18477
21.6%

Most occurring characters

ValueCountFrequency (%)
e53246
 
11.2%
o43529
 
9.2%
a31610
 
6.6%
d31603
 
6.6%
r24641
 
5.2%
l24552
 
5.2%
s24228
 
5.1%
n23111
 
4.9%
w18695
 
3.9%
i18467
 
3.9%
Other values (33)181783
38.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter388106
81.6%
Uppercase Letter85610
 
18.0%
Space Separator1749
 
0.4%

Most frequent character per category

ValueCountFrequency (%)
e53246
13.7%
o43529
11.2%
a31610
 
8.1%
d31603
 
8.1%
r24641
 
6.3%
l24552
 
6.3%
s24228
 
6.2%
n23111
 
6.0%
w18695
 
4.8%
i18467
 
4.8%
Other values (14)94424
24.3%
ValueCountFrequency (%)
F14015
16.4%
V11345
13.3%
B9572
11.2%
M8745
10.2%
O7609
8.9%
A6902
8.1%
R6887
8.0%
S3441
 
4.0%
T3440
 
4.0%
P3045
 
3.6%
Other values (8)10609
12.4%
ValueCountFrequency (%)
1749
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin473716
99.6%
Common1749
 
0.4%

Most frequent character per script

ValueCountFrequency (%)
e53246
 
11.2%
o43529
 
9.2%
a31610
 
6.7%
d31603
 
6.7%
r24641
 
5.2%
l24552
 
5.2%
s24228
 
5.1%
n23111
 
4.9%
w18695
 
3.9%
i18467
 
3.9%
Other values (32)180034
38.0%
ValueCountFrequency (%)
1749
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII475465
100.0%

Most frequent character per block

ValueCountFrequency (%)
e53246
 
11.2%
o43529
 
9.2%
a31610
 
6.6%
d31603
 
6.6%
r24641
 
5.2%
l24552
 
5.2%
s24228
 
5.1%
n23111
 
4.9%
w18695
 
3.9%
i18467
 
3.9%
Other values (33)181783
38.2%

enriched_model
Categorical

HIGH CARDINALITY

Distinct425
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size655.3 KiB
Transit
 
3858
Series 3
 
3800
Golf
 
3725
Focus
 
2554
A3
 
2241
Other values (420)
67683 

Length

Max length22
Median length6
Mean length6.278675427
Min length1

Characters and Unicode

Total characters526536
Distinct characters65
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45 ?
Unique (%)0.1%

Sample

1st rowBipper
2nd rowBipper
3rd rowSeries 4
4th rowSeries 4
5th rowNp300 Navara
ValueCountFrequency (%)
Transit3858
 
4.6%
Series 33800
 
4.5%
Golf3725
 
4.4%
Focus2554
 
3.0%
A32241
 
2.7%
Passat2139
 
2.6%
Corsa2130
 
2.5%
Transit Custom2123
 
2.5%
Series 52011
 
2.4%
A41869
 
2.2%
Other values (415)57411
68.5%
2022-05-03T12:07:51.480135image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
series8213
 
8.2%
transit7651
 
7.6%
33930
 
3.9%
golf3756
 
3.7%
focus2554
 
2.5%
passat2479
 
2.5%
a32241
 
2.2%
corsa2130
 
2.1%
custom2123
 
2.1%
52016
 
2.0%
Other values (405)63114
63.0%

Most occurring characters

ValueCountFrequency (%)
a49555
 
9.4%
s48843
 
9.3%
r40856
 
7.8%
e39273
 
7.5%
i35030
 
6.7%
o30370
 
5.8%
n27690
 
5.3%
t26615
 
5.1%
C16783
 
3.2%
16346
 
3.1%
Other values (55)195175
37.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter377676
71.7%
Uppercase Letter96028
 
18.2%
Decimal Number30229
 
5.7%
Space Separator16346
 
3.1%
Dash Punctuation6085
 
1.2%
Math Symbol172
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
C16783
17.5%
S11858
12.3%
T11641
12.1%
A10377
10.8%
M5316
 
5.5%
G5121
 
5.3%
P4804
 
5.0%
F4388
 
4.6%
R3482
 
3.6%
X3342
 
3.5%
Other values (16)18916
19.7%
ValueCountFrequency (%)
a49555
13.1%
s48843
12.9%
r40856
10.8%
e39273
10.4%
i35030
9.3%
o30370
8.0%
n27690
7.3%
t26615
7.0%
l13872
 
3.7%
c11468
 
3.0%
Other values (16)54104
14.3%
ValueCountFrequency (%)
37920
26.2%
05847
19.3%
54157
13.8%
43596
11.9%
12471
 
8.2%
62382
 
7.9%
21955
 
6.5%
81221
 
4.0%
7448
 
1.5%
9232
 
0.8%
ValueCountFrequency (%)
16346
100.0%
ValueCountFrequency (%)
-6085
100.0%
ValueCountFrequency (%)
+172
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin473704
90.0%
Common52832
 
10.0%

Most frequent character per script

ValueCountFrequency (%)
a49555
 
10.5%
s48843
 
10.3%
r40856
 
8.6%
e39273
 
8.3%
i35030
 
7.4%
o30370
 
6.4%
n27690
 
5.8%
t26615
 
5.6%
C16783
 
3.5%
l13872
 
2.9%
Other values (42)144817
30.6%
ValueCountFrequency (%)
16346
30.9%
37920
15.0%
-6085
 
11.5%
05847
 
11.1%
54157
 
7.9%
43596
 
6.8%
12471
 
4.7%
62382
 
4.5%
21955
 
3.7%
81221
 
2.3%
Other values (3)852
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII526536
100.0%

Most frequent character per block

ValueCountFrequency (%)
a49555
 
9.4%
s48843
 
9.3%
r40856
 
7.8%
e39273
 
7.5%
i35030
 
6.7%
o30370
 
5.8%
n27690
 
5.3%
t26615
 
5.1%
C16783
 
3.2%
16346
 
3.1%
Other values (55)195175
37.1%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size655.3 KiB
Diesel
68605 
Unleaded
9533 
Electric
 
3393
Premium Unleaded
 
2323
E85
 
7

Length

Max length16
Median length6
Mean length6.585027605
Min length3

Characters and Unicode

Total characters552227
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDiesel
2nd rowDiesel
3rd rowDiesel
4th rowDiesel
5th rowDiesel
ValueCountFrequency (%)
Diesel68605
81.8%
Unleaded9533
 
11.4%
Electric3393
 
4.0%
Premium Unleaded2323
 
2.8%
E857
 
< 0.1%
2022-05-03T12:07:51.668810image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2022-05-03T12:07:51.731328image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
diesel68605
79.6%
unleaded11856
 
13.8%
electric3393
 
3.9%
premium2323
 
2.7%
e857
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e166638
30.2%
l83854
15.2%
i74321
13.5%
D68605
12.4%
s68605
12.4%
d23712
 
4.3%
U11856
 
2.1%
n11856
 
2.1%
a11856
 
2.1%
c6786
 
1.2%
Other values (9)24138
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter463706
84.0%
Uppercase Letter86184
 
15.6%
Space Separator2323
 
0.4%
Decimal Number14
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
e166638
35.9%
l83854
18.1%
i74321
16.0%
s68605
14.8%
d23712
 
5.1%
n11856
 
2.6%
a11856
 
2.6%
c6786
 
1.5%
r5716
 
1.2%
m4646
 
1.0%
Other values (2)5716
 
1.2%
ValueCountFrequency (%)
D68605
79.6%
U11856
 
13.8%
E3400
 
3.9%
P2323
 
2.7%
ValueCountFrequency (%)
87
50.0%
57
50.0%
ValueCountFrequency (%)
2323
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin549890
99.6%
Common2337
 
0.4%

Most frequent character per script

ValueCountFrequency (%)
e166638
30.3%
l83854
15.2%
i74321
13.5%
D68605
12.5%
s68605
12.5%
d23712
 
4.3%
U11856
 
2.2%
n11856
 
2.2%
a11856
 
2.2%
c6786
 
1.2%
Other values (6)21801
 
4.0%
ValueCountFrequency (%)
2323
99.4%
87
 
0.3%
57
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII552227
100.0%

Most frequent character per block

ValueCountFrequency (%)
e166638
30.2%
l83854
15.2%
i74321
13.5%
D68605
12.4%
s68605
12.4%
d23712
 
4.3%
U11856
 
2.1%
n11856
 
2.1%
a11856
 
2.1%
c6786
 
1.2%
Other values (9)24138
 
4.4%

segment_global
Categorical

HIGH CORRELATION

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size655.3 KiB
Lower Medium
18174 
Upper Medium
17255 
SUV
9171 
Medium Commercial
8333 
Small Commercial
7304 
Other values (12)
23624 

Length

Max length22
Median length12
Mean length11.43647226
Min length3

Characters and Unicode

Total characters959074
Distinct characters31
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCar Derived Commercial
2nd rowCar Derived Commercial
3rd rowSports
4th rowSports
5th rowPickup
ValueCountFrequency (%)
Lower Medium18174
21.7%
Upper Medium17255
20.6%
SUV9171
10.9%
Medium Commercial8333
9.9%
Small Commercial7304
8.7%
Small6343
 
7.6%
Car Derived Commercial5663
 
6.8%
Executive4393
 
5.2%
Mini MPV1948
 
2.3%
Sports1668
 
2.0%
Other values (7)3609
 
4.3%
2022-05-03T12:07:51.934407image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
medium44432
29.6%
commercial21300
14.2%
lower18191
12.1%
upper17275
 
11.5%
small13647
 
9.1%
suv9208
 
6.1%
car5663
 
3.8%
derived5663
 
3.8%
executive4393
 
2.9%
mpv3069
 
2.0%
Other values (6)7043
 
4.7%

Most occurring characters

ValueCountFrequency (%)
e121798
12.7%
m100679
 
10.5%
i83340
 
8.7%
r69800
 
7.3%
66023
 
6.9%
u51047
 
5.3%
M50206
 
5.2%
d50095
 
5.2%
l49570
 
5.2%
o41159
 
4.3%
Other values (21)275357
28.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter718613
74.9%
Uppercase Letter174438
 
18.2%
Space Separator66023
 
6.9%

Most frequent character per category

ValueCountFrequency (%)
e121798
16.9%
m100679
14.0%
i83340
11.6%
r69800
9.7%
u51047
7.1%
d50095
7.0%
l49570
6.9%
o41159
 
5.7%
a40610
 
5.7%
p37872
 
5.3%
Other values (10)72643
10.1%
ValueCountFrequency (%)
M50206
28.8%
C26963
15.5%
U26483
15.2%
S25011
14.3%
L18231
 
10.5%
V12277
 
7.0%
D5663
 
3.2%
P4723
 
2.7%
E4393
 
2.5%
F488
 
0.3%
ValueCountFrequency (%)
66023
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin893051
93.1%
Common66023
 
6.9%

Most frequent character per script

ValueCountFrequency (%)
e121798
13.6%
m100679
11.3%
i83340
 
9.3%
r69800
 
7.8%
u51047
 
5.7%
M50206
 
5.6%
d50095
 
5.6%
l49570
 
5.6%
o41159
 
4.6%
a40610
 
4.5%
Other values (20)234747
26.3%
ValueCountFrequency (%)
66023
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII959074
100.0%

Most frequent character per block

ValueCountFrequency (%)
e121798
12.7%
m100679
 
10.5%
i83340
 
8.7%
r69800
 
7.3%
66023
 
6.9%
u51047
 
5.3%
M50206
 
5.2%
d50095
 
5.2%
l49570
 
5.2%
o41159
 
4.3%
Other values (21)275357
28.7%

segment_regional
Categorical

HIGH CORRELATION

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size655.3 KiB
C1 - lower medium -
11121 
D2 - upper medium +
9288 
Medium Commercial
8333 
C2 - lower medium +
8205 
Medium SUV
7605 
Other values (19)
39309 

Length

Max length24
Median length17
Mean length15.88322343
Min length6

Characters and Unicode

Total characters1331983
Distinct characters37
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCar Derived Van
2nd rowCar Derived Van
3rd rowSports
4th rowSports
5th rowPickup
ValueCountFrequency (%)
C1 - lower medium -11121
13.3%
D2 - upper medium +9288
11.1%
Medium Commercial8333
9.9%
C2 - lower medium +8205
9.8%
Medium SUV7605
9.1%
D1 - upper medium -6828
8.1%
Small Commercial6738
8.0%
B - small6303
7.5%
Car Derived Van5660
6.7%
E1 - large and executive4393
 
5.2%
Other values (14)9387
11.2%
2022-05-03T12:07:52.137481image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
82416
27.7%
medium52013
17.5%
lower19326
 
6.5%
upper16116
 
5.4%
commercial15627
 
5.3%
small13810
 
4.6%
c111124
 
3.7%
suv9704
 
3.3%
d29300
 
3.1%
c28205
 
2.8%
Other values (18)59798
20.1%

Most occurring characters

ValueCountFrequency (%)
213578
16.0%
e132572
 
10.0%
m132519
 
9.9%
i85705
 
6.4%
u76410
 
5.7%
r70513
 
5.3%
l67802
 
5.1%
-64923
 
4.9%
d62066
 
4.7%
a50937
 
3.8%
Other values (27)374958
28.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter846725
63.6%
Space Separator213578
 
16.0%
Uppercase Letter148571
 
11.2%
Dash Punctuation64923
 
4.9%
Decimal Number39897
 
3.0%
Math Symbol17493
 
1.3%
Other Punctuation796
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
e132572
15.7%
m132519
15.7%
i85705
10.1%
u76410
9.0%
r70513
8.3%
l67802
8.0%
d62066
7.3%
a50937
 
6.0%
o36608
 
4.3%
p35615
 
4.2%
Other values (10)95978
11.3%
ValueCountFrequency (%)
C40616
27.3%
D21795
14.7%
M21588
14.5%
S18866
12.7%
V18433
12.4%
U9704
 
6.5%
B6303
 
4.2%
P4797
 
3.2%
E4433
 
3.0%
L1240
 
0.8%
ValueCountFrequency (%)
122352
56.0%
217545
44.0%
ValueCountFrequency (%)
213578
100.0%
ValueCountFrequency (%)
-64923
100.0%
ValueCountFrequency (%)
/796
100.0%
ValueCountFrequency (%)
+17493
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin995296
74.7%
Common336687
 
25.3%

Most frequent character per script

ValueCountFrequency (%)
e132572
13.3%
m132519
13.3%
i85705
 
8.6%
u76410
 
7.7%
r70513
 
7.1%
l67802
 
6.8%
d62066
 
6.2%
a50937
 
5.1%
C40616
 
4.1%
o36608
 
3.7%
Other values (21)239548
24.1%
ValueCountFrequency (%)
213578
63.4%
-64923
 
19.3%
122352
 
6.6%
217545
 
5.2%
+17493
 
5.2%
/796
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1331983
100.0%

Most frequent character per block

ValueCountFrequency (%)
213578
16.0%
e132572
 
10.0%
m132519
 
9.9%
i85705
 
6.4%
u76410
 
5.7%
r70513
 
5.3%
l67802
 
5.1%
-64923
 
4.9%
d62066
 
4.7%
a50937
 
3.8%
Other values (27)374958
28.2%
Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size655.3 KiB
combustion
78282 
phev
 
2456
hev
 
2005
bev
 
911
mhev
 
206

Length

Max length15
Median length10
Mean length9.566198829
Min length3

Characters and Unicode

Total characters802231
Distinct characters18
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowcombustion
2nd rowcombustion
3rd rowcombustion
4th rowcombustion
5th rowcombustion
ValueCountFrequency (%)
combustion78282
93.3%
phev2456
 
2.9%
hev2005
 
2.4%
bev911
 
1.1%
mhev206
 
0.2%
alternate fuels1
 
< 0.1%
2022-05-03T12:07:52.324946image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2022-05-03T12:07:52.387423image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
combustion78282
93.3%
phev2456
 
2.9%
hev2005
 
2.4%
bev911
 
1.1%
mhev206
 
0.2%
alternate1
 
< 0.1%
fuels1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o156564
19.5%
b79193
9.9%
m78488
9.8%
t78284
9.8%
u78283
9.8%
s78283
9.8%
n78283
9.8%
c78282
9.8%
i78282
9.8%
e5581
 
0.7%
Other values (8)12708
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter802230
> 99.9%
Space Separator1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
o156564
19.5%
b79193
9.9%
m78488
9.8%
t78284
9.8%
u78283
9.8%
s78283
9.8%
n78283
9.8%
c78282
9.8%
i78282
9.8%
e5581
 
0.7%
Other values (7)12707
 
1.6%
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin802230
> 99.9%
Common1
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
o156564
19.5%
b79193
9.9%
m78488
9.8%
t78284
9.8%
u78283
9.8%
s78283
9.8%
n78283
9.8%
c78282
9.8%
i78282
9.8%
e5581
 
0.7%
Other values (7)12707
 
1.6%
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII802231
100.0%

Most frequent character per block

ValueCountFrequency (%)
o156564
19.5%
b79193
9.9%
m78488
9.8%
t78284
9.8%
u78283
9.8%
s78283
9.8%
n78283
9.8%
c78282
9.8%
i78282
9.8%
e5581
 
0.7%
Other values (8)12708
 
1.6%

Customer_Segment
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size655.3 KiB
Corporate
45877 
Small fleet
20402 
Governments
7155 
Private Households
6729 
Insurance companies
 
3608
Other values (2)
 
90

Length

Max length19
Median length9
Mean length10.80540418
Min length5

Characters and Unicode

Total characters906152
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSmall fleet
2nd rowSmall fleet
3rd rowPrivate Households
4th rowPrivate Households
5th rowSmall fleet
ValueCountFrequency (%)
Corporate45877
54.7%
Small fleet20402
24.3%
Governments7155
 
8.5%
Private Households6729
 
8.0%
Insurance companies3608
 
4.3%
Banks88
 
0.1%
Own fleet2
 
< 0.1%
2022-05-03T12:07:52.543638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2022-05-03T12:07:52.606121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
corporate45877
40.0%
fleet20404
17.8%
small20402
17.8%
governments7155
 
6.2%
private6729
 
5.9%
households6729
 
5.9%
insurance3608
 
3.1%
companies3608
 
3.1%
banks88
 
0.1%
own2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e121669
13.4%
o115975
12.8%
r109246
12.1%
a80312
8.9%
t80165
8.8%
l67937
 
7.5%
p49485
 
5.5%
C45877
 
5.1%
m31165
 
3.4%
30741
 
3.4%
Other values (18)173580
19.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter784821
86.6%
Uppercase Letter90590
 
10.0%
Space Separator30741
 
3.4%

Most frequent character per category

ValueCountFrequency (%)
e121669
15.5%
o115975
14.8%
r109246
13.9%
a80312
10.2%
t80165
10.2%
l67937
8.7%
p49485
6.3%
m31165
 
4.0%
s27917
 
3.6%
n25224
 
3.2%
Other values (9)75726
9.6%
ValueCountFrequency (%)
C45877
50.6%
S20402
22.5%
G7155
 
7.9%
P6729
 
7.4%
H6729
 
7.4%
I3608
 
4.0%
B88
 
0.1%
O2
 
< 0.1%
ValueCountFrequency (%)
30741
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin875411
96.6%
Common30741
 
3.4%

Most frequent character per script

ValueCountFrequency (%)
e121669
13.9%
o115975
13.2%
r109246
12.5%
a80312
9.2%
t80165
9.2%
l67937
7.8%
p49485
 
5.7%
C45877
 
5.2%
m31165
 
3.6%
s27917
 
3.2%
Other values (17)145663
16.6%
ValueCountFrequency (%)
30741
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII906152
100.0%

Most frequent character per block

ValueCountFrequency (%)
e121669
13.4%
o115975
12.8%
r109246
12.1%
a80312
8.9%
t80165
8.8%
l67937
 
7.5%
p49485
 
5.5%
C45877
 
5.1%
m31165
 
3.4%
30741
 
3.4%
Other values (18)173580
19.2%

Unnamed: 47
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing83861
Missing (%)100.0%
Memory size655.3 KiB

Unnamed: 48
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing83861
Missing (%)100.0%
Memory size655.3 KiB

Unnamed: 49
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing83861
Missing (%)100.0%
Memory size655.3 KiB

Unnamed: 50
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing83861
Missing (%)100.0%
Memory size655.3 KiB

Unnamed: 51
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing83861
Missing (%)100.0%
Memory size655.3 KiB

Unnamed: 52
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing83861
Missing (%)100.0%
Memory size655.3 KiB

Interactions

2022-05-03T12:06:41.702448image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:41.827418image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:41.936768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:42.061738image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:42.171087image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:42.280430image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:42.389783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:42.514753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:42.616793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:42.743020image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:42.852364image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:42.977305image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:43.102306image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:43.211656image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:43.321004image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:43.430353image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:43.539704image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:43.665969image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:43.766858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:43.891864image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:44.003375image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:44.143938image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:44.375457image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:44.484836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:44.594186image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:44.704831image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:44.798557image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:44.907905image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:45.017258image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:45.126604image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:45.235956image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:45.345310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:45.439030image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:45.548379image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:45.657729image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:45.768370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:45.877719image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:45.987069image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:46.096418image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:46.205768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:46.315117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:46.424467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:46.533815image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:46.658786image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:46.769415image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:46.878763image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:47.003739image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:47.113083image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:47.222433image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:47.347403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:47.456745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:47.566103image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:47.692304image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:47.801658image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:47.926594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:48.035975image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:48.160946image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:48.270294image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:48.395265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:48.504615image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:48.613965image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:48.740095image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:48.865064image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:48.976623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:49.085974image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:49.228611image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:49.347044image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:49.472014image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:49.581361image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:49.691869image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:49.816839image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:49.926188image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:50.035537image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:50.160508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:50.269857image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:50.519802image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:50.629147image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:50.739727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:50.864698image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:50.974043image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:51.083392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:51.208363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:51.317712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:51.442684image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:51.552031image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:51.662562image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:51.777234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:51.883832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:51.995687image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:52.105036image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:52.214385image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:52.323736image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:52.448705image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:52.542435image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:52.661541image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:52.770923image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:52.895893image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:53.005248image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:53.114594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:53.223944image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:53.333289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:53.442641image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:53.551987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:53.678132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:53.787480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:53.896829image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:54.007723image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:54.132694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:54.259727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:54.390654image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:54.500002image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:54.609351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:54.735487image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:54.844836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:54.954185image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:55.072872image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:55.182222image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:55.291573image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:55.416528image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:55.525860image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:55.635210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:55.760809image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:55.870191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:55.995161image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:56.104510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:56.213863image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:56.338834image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:56.448188image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:56.557531image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:56.668074image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:56.777459image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:56.886805image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:56.996156image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:57.121126image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:57.230481image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:57.339825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:57.449172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:57.558521image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:57.669752image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:57.779134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:58.044697image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:58.154045image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:58.278986image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:58.388367image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:58.497712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:58.622687image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:58.733251image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:58.842600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:58.964055image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:59.073436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:59.216071image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:59.331285image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:59.458850image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:59.568193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:59.678836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:59.788185image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:06:59.913155image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:00.022504image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:00.147474image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:00.256823image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:00.366174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:00.491144image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:00.616085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:00.773495image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:00.898436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:01.023406image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:01.148376image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:01.257726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:01.382696image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:01.507667image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:01.617016image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:01.752566image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:01.861915image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:01.986885image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:02.096235image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:02.221205image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:02.330554image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:02.455525image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:02.580495image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:02.699733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:02.840324image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:02.965294image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:03.090265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:03.199613image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:03.324585image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:03.433933image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:03.558905image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:03.685101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:03.794452image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:03.919422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:04.025197image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:04.150143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:04.277194image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:04.403906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:04.513259image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:04.606988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:04.717595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:04.826949image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:04.951916image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:05.061268image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:05.170614image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:05.279965image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:05.389309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:05.498630image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:05.592390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:05.712573image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:05.821921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:05.931272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:06.040621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:06.149971image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:06.259325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:06.368670image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:06.478017image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:06.587365image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:06.697973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:06.822943image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:06.932293image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:07.041643image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:07.150991image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:07.463418image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:07.572767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:07.683296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:07.793899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:07.903247image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:08.012595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:08.121946image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:08.231295image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:08.340612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:08.449993image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:08.574964image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:08.685578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:08.794927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:08.904276image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:09.016467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:09.125816image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:09.268483image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:09.386417image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:09.495799image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:09.605146image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:09.715714image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:09.825058image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:09.934411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:10.051743image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:10.161124image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:10.270473image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:10.379822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:10.504794image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:10.614145image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:10.709121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:10.834092image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:10.943437image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:11.052790image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:11.162137image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:11.287109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:11.396457image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:11.521427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:11.630777image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:11.741995image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:11.866966image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:11.976309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:12.101291image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:12.210634image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:12.335605image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:12.444950image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:12.569921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:12.680527image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:12.789878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:12.914848image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:13.024195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:13.149166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:13.258515image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:13.383485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:13.508456image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:13.633426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:13.744029image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:13.853375image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:13.965808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:14.075193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:14.217876image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:14.342878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:14.461560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:14.570911image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:14.697151image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:14.806503image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:14.915849image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:15.025199image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:15.146007image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:15.271009image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:15.380359image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:15.489709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:15.599057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:15.725297image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:15.834654image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:15.959623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:16.068968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:16.178318image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:16.287666image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:16.412638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:16.521986image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:16.631336image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:16.741946image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:16.851295image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:16.960644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:17.069993image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:17.179343image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:17.288693image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:17.398044image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:17.523014image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:17.632361image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:17.742959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:17.852312image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:17.961660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:18.071008image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:18.195978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:18.305328image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:18.665826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:18.790828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:18.900178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:19.011801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:19.121151image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:19.263848image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:19.388290image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:19.513293image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:19.622643image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:19.748843image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:19.858190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:19.967538image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:20.076888image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:20.201858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:20.311207image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:20.420557image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:20.545527image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:20.654881image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:20.765510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:20.890486image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:20.999837image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:21.109182image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:21.234157image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:21.343508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:21.468475image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:21.577825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:21.688456image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:21.797807image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:21.922776image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:22.032126image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:22.157067image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:22.266415image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:22.391386image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:22.516354image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:22.641325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:22.751909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:22.876852image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:22.986228image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:23.095572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:23.220552image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:23.329895image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:23.454866image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:23.564220image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:23.674700image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:23.794963image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:23.919966image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:24.029438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:24.138790image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:24.281478image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:24.401487image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:24.510866image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:24.620221image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:24.730738image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:24.840081image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:24.949435image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:25.058782image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:25.168132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:25.277480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:25.371209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:25.480560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:25.589904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:25.700495image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:25.809842image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:25.919191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:26.044161image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:26.153512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:26.262861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:26.372209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:26.481562image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:26.590906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:26.701542image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:26.826513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:26.935860image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:27.045209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:27.154558image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:27.263914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:27.388878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:27.498230image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:27.607581image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:27.718173image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:27.843144image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:27.952494image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:28.061844image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:28.171194image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:28.296164image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:28.405499image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:28.530481image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:28.671143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:28.781629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:28.906600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:29.018232image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:29.136482image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:29.294813image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:29.426648image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:29.551652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:29.662254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:29.787258image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:29.912228image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:30.021577image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:30.146548image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:30.255896image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:30.380867image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:30.490219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:30.615190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:30.741423image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:30.850772image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:30.975743image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:31.085092image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:31.194441image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:31.319416image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:31.428761image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:31.538112image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:31.664330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:31.773712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:31.898682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:32.008030image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:32.125803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:32.235151image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:32.641307image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:32.767552image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:32.876905image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:32.993844image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:33.103226image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:33.228196image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:33.337545image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:33.446895image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:33.571866image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:33.698109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:33.807459image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:33.932424image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:34.044783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:34.169753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:34.313137image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:34.449190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:34.575264image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:34.703062image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:34.811601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:34.936570image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:35.057649image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:35.182619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:35.311513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:35.420862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:35.545833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:35.656516image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:35.782665image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:35.907288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-05-03T12:07:36.016643image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2022-05-03T12:07:52.732336image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-03T12:07:53.044765image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-03T12:07:53.341568image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-03T12:07:53.653994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2022-05-03T12:07:53.985516image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2022-05-03T12:07:36.627177image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-03T12:07:39.462853image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-05-03T12:07:40.869548image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

srnocustomer_numbercustomer_idcustomer_local_keybranch_desc_consbusiness_classification_descdriver_namestart_date_leasecontractend_date_leasecontractleasecontract_status_conscustomer_driver_idlease_contract_idleasecontract_numbervehicle_idvehicle_local_keymake_consregistration_datemodel_consmodel_type_descmanufacturer_groupvehicle_type_consvehicle_segment_descprimary_fuel_type_consfuel_consump_manufacturercat_price_eurocat_price_options_eurocat_price_accessories_europroduct_name_consengine_capacityengine_powergross_weighttare_weightco2_level_combinednumber_of_cylindersnumber_of_doorscontract_durationcontract_mileagemonthly_lease_installmentsupplier_discount_percdiscount_to_customerenriched_makeenriched_modelenriched_primary_fuel_typesegment_globalsegment_regionalnew_powertrain_typeCustomer_SegmentUnnamed: 47Unnamed: 48Unnamed: 49Unnamed: 50Unnamed: 51Unnamed: 52
0154950655028898981UK-C-506550Furniture And FixturesSmall fleettoby downes2014-03-10 00:00:002018-04-09 00:00:00Ended000000000000049991851664379439877167355176170UK-9877167Peugeot2013-12-31BIPPER1.3D75 PROFPSAVanCar derived vanDiesel0.0013580.600.00118.29Full service Closed calculation12485517501090119404840000144.259.005526.90PeugeotBipperDieselCar Derived CommercialCar Derived VancombustionSmall fleetNaNNaNNaNNaNNaNNaN
1155050655028898981UK-C-506550Furniture And FixturesSmall fleettoby downes2018-04-10 00:00:002019-04-08 00:00:00Ended000000000000049991853308808531177401459686869UK-1177401Peugeot2013-12-31BIPPER1.3D75 PROFPSAVanCar derived vanDiesel0.0013580.600.000.00Full service Closed calculation1248551750109011940128000137.010.000.00PeugeotBipperDieselCar Derived CommercialCar Derived VancombustionSmall fleetNaNNaNNaNNaNNaNNaN
2155384198432193564UK-C-841984Private HouseholdsPrivate Householdsthomas fosmes2016-04-25 00:00:002018-05-24 00:00:00Ended000000000000051747542702726841051210413736656UK-1051210BMW2016-03-314 SERIES2.0 M SPT PRF ABMW GroupCarMedium carDiesel1.5632858.23484.050.00Full service Closed calculation199511020051485117422416000280.2419.007571.25BmwSeries 4DieselSportsSportscombustionPrivate HouseholdsNaNNaNNaNNaNNaNNaN
3155484198432193564UK-C-841984Private HouseholdsPrivate Householdsthomas fosmes2018-05-25 00:00:002018-10-08 00:00:00Ended000000000000051747543328667471183042461405173UK-1183042BMW2016-03-314 SERIES2.0 M SPT PRF ABMW GroupCarMedium carDiesel1.5632858.230.000.00Full service Closed calculation199511020051485117421214000370.740.000.00BmwSeries 4DieselSportsSportscombustionPrivate HouseholdsNaNNaNNaNNaNNaNNaN
4155585074232852015UK-C-850742Special Trade ContractorsSmall fleetterry cottam2016-09-26 00:00:002019-09-25 00:00:00Ended000000000000052104352790715101083812423903120UK-1083812Nissan2016-07-31NP300190 D/C TEKNA ARenault-NissanVanOther VanDiesel0.0028272.07393.260.00Full service Closed calculation229814030101958183403660000298.4025.086377.49NissanNp300 NavaraDieselPickupPickupcombustionSmall fleetNaNNaNNaNNaNNaNNaN
5155685074232852015UK-C-850742Special Trade ContractorsSmall fleetterry cottam2019-09-26 00:00:00###############################################################################################################################################################################################################################################################Operational000000000000052104353698911431289414487420561UK-1289414Nissan2016-07-31NP300190 D/C TEKNA ARenault-NissanVanOther VanDiesel0.0028272.070.000.00Full service Closed calculation229814030101958183401220000333.050.000.00NissanNp300 NavaraDieselPickupPickupcombustionSmall fleetNaNNaNNaNNaNNaNNaN
6156281469929954544UK-C-814699Private HouseholdsSmall fleetterence podmore2017-04-21 00:00:002018-04-12 00:00:00Ended000000000000050009172985387121115966438920711UK-1115966Audi2014-03-19Q52.0D177 S-LINE+VAGCarSUVDiesel0.0033988.550.000.00Full service Closed calculation196813023651755154451210000353.100.000.00AudiQ5DieselSUVMedium SUVcombustionSmall fleetNaNNaNNaNNaNNaNNaN
7156381469929954544UK-C-814699Private HouseholdsSmall fleetterence podmore2014-03-21 00:00:002017-04-20 00:00:00Ended000000000000050009171665690969879025355715224UK-9879025Audi2014-03-19Q52.0D177 S-LINE+VAGCarSUVDiesel0.0033988.55481.590.00Full service Closed calculation196813023651755154453630000383.6412.003629.99AudiQ5DieselSUVMedium SUVcombustionSmall fleetNaNNaNNaNNaNNaNNaN
8163084556232348357UK-C-845562Private HouseholdsPrivate Householdssusan fletcher2019-06-13 00:00:00###############################################################################################################################################################################################################################################################Operational000000000000051857323645700601267425482301484UK-1267425Mini2016-06-13MINI1.5BMW GroupCarSmall carPetrol1.7417168.000.000.00Full service Closed calculation1499100167012051143264000168.370.000.00MiniMiniUnleadedSportsSportscombustionPrivate HouseholdsNaNNaNNaNNaNNaNNaN
9163184556232348357UK-C-845562Private HouseholdsPrivate Householdssusan fletcher2016-06-13 00:00:002019-06-12 00:00:00Ended000000000000051857322731746751061966417380645UK-1061966Mini2016-06-13MINI1.5BMW GroupCarSmall carPetrol1.7417168.000.000.00Full service Closed calculation149910016701205114323624000160.000.002088.33MiniMiniUnleadedSportsSportscombustionPrivate HouseholdsNaNNaNNaNNaNNaNNaN

Last rows

srnocustomer_numbercustomer_idcustomer_local_keybranch_desc_consbusiness_classification_descdriver_namestart_date_leasecontractend_date_leasecontractleasecontract_status_conscustomer_driver_idlease_contract_idleasecontract_numbervehicle_idvehicle_local_keymake_consregistration_datemodel_consmodel_type_descmanufacturer_groupvehicle_type_consvehicle_segment_descprimary_fuel_type_consfuel_consump_manufacturercat_price_eurocat_price_options_eurocat_price_accessories_europroduct_name_consengine_capacityengine_powergross_weighttare_weightco2_level_combinednumber_of_cylindersnumber_of_doorscontract_durationcontract_mileagemonthly_lease_installmentsupplier_discount_percdiscount_to_customerenriched_makeenriched_modelenriched_primary_fuel_typesegment_globalsegment_regionalnew_powertrain_typeCustomer_SegmentUnnamed: 47Unnamed: 48Unnamed: 49Unnamed: 50Unnamed: 51Unnamed: 52
8385161983457524084075UK-C-34575Not consolidatedGovernments** 20T ***MARTIN SANG2013-07-23 00:00:002018-08-29 00:00:00EndedN6850U1591720669805187337040763UK-9805187Opel2013-06-12MOVANOD125 L2H1 D/SGMVanCommercial vanDiesel0.028634.330.000.00Full service Closed calculation2298923500218704060100000331.610.00.00OpelMovanoDieselMedium CommercialMedium CommercialcombustionGovernmentsNaNNaNNaNNaNNaNNaN
8385261992179923494184UK-C-21799Special Trade ContractorsSmall fleet* UNSPECIFIED2010-10-16 00:00:002011-04-19 00:00:00EndedUNSPEC1335517628809202244723074UK-8809202Audi2006-10-16A32.0D140 S-LINEVAGCarCompact carDiesel0.020712.540.000.00Full service Closed calculation1968103194013801464565000248.590.00.00AudiA3DieselLower MediumC2 - lower medium +combustionSmall fleetNaNNaNNaNNaNNaNNaN
8385362002973823495987UK-C-29738Private HouseholdsPrivate Households* UNSPEC2011-06-23 00:00:002012-06-21 00:00:00EndedUNSPEC1337162059246031244772840UK-9246031Fiat2008-03-27GRANDE PUNTO1.2 8v ACTIVEFiat GroupCarSmall carPetrol0.07565.650.000.00Full service Closed calculation12424815751015139431212000155.370.00.00FiatPuntoPremium UnleadedSmallB - smallcombustionPrivate HouseholdsNaNNaNNaNNaNNaNNaN
83854620190945823605658UK-C-909458Not consolidatedGovernments* CARPOOL 12011-06-01 00:00:002017-06-06 00:00:00Ended11337175549245168244772772UK-9245168Mercedes Benz2005-11-23SPRINTER311CDi VAN 3.5TDaimler AGVanCommercial vanDiesel0.022163.670.000.00Full service Closed calculation214880350019450451220000141.600.00.00MercedesSprinterDieselMedium CommercialMedium CommercialcombustionGovernmentsNaNNaNNaNNaNNaNNaN
83855620290945823605658UK-C-909458Not consolidatedGovernments* CARPOOL 12011-06-01 00:00:002011-09-19 00:00:00Ended11337175509245118244773243UK-9245118Mercedes Benz2005-11-23SPRINTER311CDi VAN 3.5TDaimler AGVanCommercial vanDiesel0.022163.670.000.00Full service Closed calculation214880350019450451220000141.600.00.00MercedesSprinterDieselMedium CommercialMedium CommercialcombustionGovernmentsNaNNaNNaNNaNNaNNaN
83856620390945823605658UK-C-909458Not consolidatedGovernments* CARPOOL 12011-06-01 00:00:002017-06-06 00:00:00Ended11337175459245061244772767UK-9245061Mercedes Benz2005-11-23SPRINTER311CDi VAN 3.5TDaimler AGVanCommercial vanDiesel0.022163.670.000.00Full service Closed calculation214880350019450451220000141.600.00.00MercedesSprinterDieselMedium CommercialMedium CommercialcombustionGovernmentsNaNNaNNaNNaNNaNNaN
83857620490945823605658UK-C-909458Not consolidatedGovernments* CARPOOL 12011-06-01 00:00:002017-06-06 00:00:00Ended11337175519245132244773244UK-9245132Mercedes Benz2005-11-23SPRINTER311CDi VAN 3.5TDaimler AGVanCommercial vanDiesel0.022163.670.000.00Full service Closed calculation214880350019450451220000141.600.00.00MercedesSprinterDieselMedium CommercialMedium CommercialcombustionGovernmentsNaNNaNNaNNaNNaNNaN
8385862052432423499666UK-C-24324Business ServicesCorporate(VAN) STEVE BLAKE2010-09-16 00:00:002016-03-01 00:00:00Ended1008021335401448573542244647270UK-8573542Peugeot2010-09-16PARTNER1.6D90 850 S FPPSAVanCar derived vanDiesel0.014303.260.001033.08Open Calculation15606621801332153404845416254.9210.54567.08PeugeotPartnerDieselCar Derived CommercialCar Derived VancombustionCorporateNaNNaNNaNNaNNaNNaN
8385962062432423499666UK-C-24324Business ServicesCorporate(VAN) STEVE BLAKE2011-09-30 00:00:002015-12-01 00:00:00Ended1008021337264669394652244787723UK-9394652Peugeot2011-09-30PARTNER1.6D92 850 SPSAVanCar derived vanDiesel0.014470.030.002696.45Open Calculation15606621701305136454835560255.9911.04997.85PeugeotPartnerDieselCar Derived CommercialCar Derived VancombustionCorporateNaNNaNNaNNaNNaNNaN
8386062072432423499666UK-C-24324Business ServicesCorporate(VAN) STEVE BLAKE2011-12-07 00:00:002016-03-16 00:00:00Ended1008021337173239243006244733435UK-9243006Ford2011-12-05TRANSIT CONNECT1.8D90 200Ford Motor Co.VanCar derived vanDiesel0.015287.2044.52812.16Open Calculation17536620401427164404826080245.0511.04881.25FordTransit ConnectDieselCar Derived CommercialCar Derived VancombustionCorporateNaNNaNNaNNaNNaNNaN